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  • 1.
    Aarno, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ekvall, Staffan
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Adaptive virtual fixtures for machine-assisted teleoperation tasks2005In: 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4, 2005, p. 1139-1144Conference paper (Refereed)
    Abstract [en]

    It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.

  • 2.
    Aarno, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Layered HMM for motion intention recognition2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 5130-5135Conference paper (Refereed)
    Abstract [en]

    Acquiring, representing and modeling human skins is one of the key research areas in teleoperation, programming. by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtasks in order to provide manageable modeling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classilication performance with respect to noise, number of gestems, type of HAIM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi dimensional HMMs for gestem classification is small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers.

  • 3.
    Abdul Khader, Shahbaz
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Data-Driven Methods for Contact-Rich Manipulation: Control Stability and Data-Efficiency2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Autonomous robots are expected to make a greater presence in the homes and workplaces of human beings. Unlike their industrial counterparts, autonomous robots have to deal with a great deal of uncertainty and lack of structure in their environment. A remarkable aspect of performing manipulation in such a scenario is the possibility of physical contact between the robot and the environment. Therefore, not unlike human manipulation, robotic manipulation has to manage contacts, both expected and unexpected, that are often characterized by complex interaction dynamics.

    Skill learning has emerged as a promising approach for robots to acquire rich motion generation capabilities. In skill learning, data driven methods are used to learn reactive control policies that map states to actions. Such an approach is appealing because a sufficiently expressive policy can almost instantaneously generate appropriate control actions without the need for computationally expensive search operations. Although reinforcement learning (RL) is a natural framework for skill learning, its practical application is limited for a number of reasons. Arguably, the two main reasons are the lack of guaranteed control stability and poor data-efficiency. While control stability is necessary for ensuring safety and predictability, data-efficiency is required for achieving realistic training times. In this thesis, solutions are sought for these two issues in the context of contact-rich manipulation.

    First, this thesis addresses the problem of control stability. Despite unknown interaction dynamics during contact, skill learning with stability guarantee is formulated as a model-free RL problem. The thesis proposes multiple solutions for parameterizing stability-aware policies. Some policy parameterizations are partly or almost wholly deep neural networks. This is followed by policy search solutions that preserve stability during random exploration, if required. In one case, a novel evolution strategies-based policy search method is introduced. It is shown, with the help of real robot experiments, that Lyapunov stability is both possible and beneficial for RL-based skill learning.

    Second, this thesis addresses the issue of data-efficiency. Although data-efficiency is targeted by formulating skill learning as a model-based RL problem, only the model learning part is addressed. In addition to benefiting from the data-efficiency and uncertainty representation of the Gaussian process, this thesis further investigates the benefits of adopting the structure of hybrid automata for learning forward dynamics models. The method also includes an algorithm for predicting long-term trajectory distributions that can represent discontinuities and multiple modes. The proposed method is shown to be more data-efficient than some state-of-the-art methods. 

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  • 4.
    Abdul Khader, Shahbaz
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yin, Hang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Falco, Pietro
    ABB Corporate Research, Vasteras, 72178, Sweden.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. ABB Corporate Research, Vasteras, 72178, Sweden.
    Learning deep energy shaping policies for stability-guaranteed manipulation2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 4, p. 8583-8590Article in journal (Refereed)
    Abstract [en]

    Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on passivity. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.

  • 5.
    Abdul Khader, Shahbaz
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yin, Hang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Falco, Pietro
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Learning Deep Neural Policies with Stability GuaranteesManuscript (preprint) (Other academic)
    Abstract [en]

    Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, unconditional stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on passivity. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.

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  • 6.
    Abdul Khader, Shahbaz
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. ABB Corp Res, Västerås, Sweden..
    Yin, Hang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Falco, Pietro
    ABB Corp Res, Västerås, Sweden..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Learning Stable Normalizing-Flow Control for Robotic Manipulation2021In: 2021 IEEE International Conference On Robotics And Automation (ICRA 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 1644-1650Conference paper (Refereed)
    Abstract [en]

    Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does not exist in general, especially one that scales to complex manipulation tasks. We contribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deep RL algorithms. While stable exploration is not guaranteed, our method is designed to ultimately produce deterministic controllers with provable stability. In addition to demonstrating our method on challenging contact-rich manipulation tasks, we also show that it is possible to achieve considerable exploration efficiency-reduced state space coverage and actuation efforts- without losing learning efficiency.

  • 7.
    Abdul Khader, Shahbaz
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yin, Hang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Pietro, Falco
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Learning Stable Normalizing-Flow Control for Robotic ManipulationManuscript (preprint) (Other academic)
    Abstract [en]

    Reinforcement Learning (RL) of robotic manipu-lation skills, despite its impressive successes, stands to benefitfrom incorporating domain knowledge from control theory. Oneof the most important properties that is of interest is controlstability. Ideally, one would like to achieve stability guaranteeswhile staying within the framework of state-of-the-art deepRL algorithms. Such a solution does not exist in general,especially one that scales to complex manipulation tasks. Wecontribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deepRL algorithms. While stable exploration is not guaranteed,our method is designed to ultimately produce deterministiccontrollers with provable stability. In addition to demonstratingour method on challenging contact-rich manipulation tasks, wealso show that it is possible to achieve considerable explorationefficiency–reduced state space coverage and actuation efforts–without losing learning efficiency.

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  • 8. Agarwal, Priyanshu
    et al.
    Al Moubayed, Samer
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Alspach, Alexander
    Kim, Joohyung
    Carter, Elizabeth J.
    Lehman, Jill Fain
    Yamane, Katsu
    Imitating Human Movement with Teleoperated Robotic Head2016In: 2016 25TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), 2016, p. 630-637Conference paper (Refereed)
    Abstract [en]

    Effective teleoperation requires real-time control of a remote robotic system. In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head [1], which we call TeleFurhat. The controller uses the head motion of an operator measured by a Microsoft Kinect 2 sensor as reference and applies a processing framework to condition and render the motion on the robot head. The processing framework includes a pre-filter based on a moving average filter, a neural network-based model for improving the accuracy of the raw pose measurements of Kinect, and a constrained-state Kalman filter that uses a minimum jerk model to smooth motion trajectories and limit the magnitude of changes in position, velocity, and acceleration. Our results demonstrate that the robot can reproduce the human head motion in real time with a latency of approximately 100 to 170 ms while operating within its physical limits. Furthermore, viewers prefer our new method over rendering the raw pose data from Kinect.

  • 9. Aguiar, Miguel
    et al.
    Estrela da Silva, Jorge
    Borges de Sousa, João
    Minimal time delivery of multiple robots2020In: 2020 59th IEEE Conference on Decision and Control (CDC), 2020Conference paper (Refereed)
    Abstract [en]

    Consider a set of autonomous vehicles, each one with a preassigned task to start at a given region. Due to energy constraints, and in order to minimize the overall task completion time, these vehicles are deployed from a faster carrier vehicle. This paper develops a dynamic programming (DP) based solution for the problem of finding the optimal deployment location and time for each vehicle, and for a given sequence of deployments, so that the global mission duration is minimal. The problem is specialized for ocean-going vehicles operating under time-varying currents. The solution approach involves solving a sequence of optimal stopping problems that are transformed into a set variational inequalities through the application of the dynamic programming principle (DPP). The optimal trajectory for the carrier and the optimal deployment location and time for each vehicle to be deployed are obtained in feedback-form from the numerical solution of the variational inequalities. The solution is computed with our open source parallel implementation of the fast sweeping method. The approach is illustrated with two numerical examples.

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  • 10.
    Ahlberg, Sofie
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Axelsson, Agnes
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Yu, Pian
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Shaw Cortez, Wenceslao E.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Gao, Yuan
    Uppsala Univ, Dept Informat Technol, Uppsala, Sweden.;Shenzhen Inst Artificial Intelligence & Robot Soc, Ctr Intelligent Robots, Shenzhen, Peoples R China..
    Ghadirzadeh, Ali
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Castellano, Ginevra
    Uppsala Univ, Dept Informat Technol, Uppsala, Sweden..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Skantze, Gabriel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Co-adaptive Human-Robot Cooperation: Summary and Challenges2022In: Unmanned Systems, ISSN 2301-3850, E-ISSN 2301-3869, Vol. 10, no 02, p. 187-203Article in journal (Refereed)
    Abstract [en]

    The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human-robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human-robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.

  • 11.
    Alberti, Marina
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Chemical Science and Engineering (CHE).
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Relational approaches for joint object classification andscene similarity measurement in indoor environments2014In: Proc. of 2014 AAAI Spring Symposium QualitativeRepresentations for Robots 2014, Palo Alto, California: The AAAI Press , 2014Conference paper (Refereed)
    Abstract [en]

    The qualitative structure of objects and their spatial distribution,to a large extent, define an indoor human environmentscene. This paper presents an approach forindoor scene similarity measurement based on the spatialcharacteristics and arrangement of the objects inthe scene. For this purpose, two main sets of spatialfeatures are computed, from single objects and objectpairs. A Gaussian Mixture Model is applied both onthe single object features and the object pair features, tolearn object class models and relationships of the objectpairs, respectively. Given an unknown scene, the objectclasses are predicted using the probabilistic frameworkon the learned object class models. From the predictedobject classes, object pair features are extracted. A fi-nal scene similarity score is obtained using the learnedprobabilistic models of object pair relationships. Ourmethod is tested on a real world 3D database of deskscenes, using a leave-one-out cross-validation framework.To evaluate the effect of varying conditions on thescene similarity score, we apply our method on mockscenes, generated by removing objects of different categoriesin the test scenes.

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  • 12.
    Alexanderson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    House, David
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    Automatic annotation of gestural units in spontaneous face-to-face interaction2016In: MA3HMI 2016 - Proceedings of the Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, 2016, p. 15-19Conference paper (Refereed)
    Abstract [en]

    Speech and gesture co-occur in spontaneous dialogue in a highly complex fashion. There is a large variability in the motion that people exhibit during a dialogue, and different kinds of motion occur during different states of the interaction. A wide range of multimodal interface applications, for example in the fields of virtual agents or social robots, can be envisioned where it is important to be able to automatically identify gestures that carry information and discriminate them from other types of motion. While it is easy for a human to distinguish and segment manual gestures from a flow of multimodal information, the same task is not trivial to perform for a machine. In this paper we present a method to automatically segment and label gestural units from a stream of 3D motion capture data. The gestural flow is modeled with a 2-level Hierarchical Hidden Markov Model (HHMM) where the sub-states correspond to gesture phases. The model is trained based on labels of complete gesture units and self-adaptive manipulators. The model is tested and validated on two datasets differing in genre and in method of capturing motion, and outperforms a state-of-the-art SVM classifier on a publicly available dataset.

  • 13.
    Alhusin Alkhdur, Abdullah
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Intelligent human-robot assembly enabled by brain EEG2021In: Advanced Human-Robot Collaboration in Manufacturing, Springer Nature , 2021, p. 351-371Chapter in book (Other academic)
    Abstract [en]

    This chapter reports a framework that can facilitate the interactions between a human's EEG (electroencephalography) signals and an industrial robot. This can be achieved by using an EEG headset that captures the brain signals of the human and send it via Bluetooth to a local workstation for signal processing, feature extraction and classification. The system developed provides the ability for a shop-floor operator to control the robot using own brain signals. The system can cooperate with other channels of communications (gesture, voice, etc.) to strengthen the collaboration between the human and the robot during shared assembly operations. Such a collaboration aims to fuse the high accuracy of the robot with the high versatility of the human. Therefore, the aim is to exploit the strength of both sides and enhance the quality and adaptability of human-robot collaborative assembly operations. This approach is applicable to other types of robots as well, for example ones used for assisting people with severe disability.

  • 14.
    Alhusin Alkhdur Mohammed, Abdullah
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Advanced human-robot collaborative assembly using electroencephalogram signals of human brains2020In: Procedia CIRP, Elsevier B.V. , 2020, p. 1200-1205Conference paper (Refereed)
    Abstract [en]

    This paper introduces an intelligent system that can manipulate an industrial robot using the electroencephalogram signals of human brains to perform collaborative assembly tasks. The system is initiated by capturing the brain signals using a wearable headset, and the signals are then filtered to remove any possible artifact. Consequently, the process continues by identifying the brain signals patterns using a classifier based on pre-recorded samples. The classifier's output determines the proper matching of the robot command that is intended by the human. To validate the results, an industrial collaborative assembly scenario of a car manifold is examined as a case study. 

  • 15.
    Al-Janabi, Mustafa
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Trajectory Optimization of Smart City Scenarios Using Learning Model Predictive Control2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Smart cities embrace cutting-edge technologies to improve transportation efficiency and safety. With the rollout of 5G and an ever-growing network of connected infrastructure sensors, real-time road condition awareness is becoming a reality. However, this progress brings new challenges. The communication and vast amounts of data generated by autonomous vehicles and the connected infrastructure must be navigated. Furthermore, different levels of autonomous driving (ranging from 0 to 5) are rolled out gradually and human-driven vehicles will continue to share the roads with autonomous vehicles for some time. In this work, we apply a data-driven control scheme called Learning Model Predictive Control (LMPC) to three different smart city scenarios of increasing complexity. Given a successful execution of a scenario, LMPC uses the trajectory data from previous executions to improve the performance of subsequent executions while guaranteeing safety and recursive feasibility. Furthermore, the performance from one execution to another is guaranteed to be non-decreasing. For our three smart-city scenarios, we apply a minimum time objective and start with a single vehicle in a two-lane intersection. Then, we add an obstacle on the lane of the ego vehicle, and lastly, we add oncoming traffic. We find that LMPC gives us improved traffic efficiency with shorter travel. However, we find that LMPC may not be suitable for real-time training in smart city scenarios. Thus, we conclude that this approach is suitable for simulator-driven, offline, training on any trajectory data that might be generated from autonomous vehicles and the infrastructure sensors in future smart cities. Over time, this can be used to construct large data sets of optimal trajectories which are available for the connected vehicles in most urban scenarios.

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  • 16.
    Allshire, Arthur
    et al.
    Univ Toronto, Vector Inst, Toronto, ON, Canada.;Nvidia, Santa Clara, CA 95051 USA..
    Mittal, Mayank
    Nvidia, Santa Clara, CA 95051 USA.;ETH, Zurich, Switzerland..
    Lodaya, Varun
    Univ Toronto, Vector Inst, Toronto, ON, Canada..
    Makoviychuk, Viktor
    Nvidia, Santa Clara, CA 95051 USA..
    Makoviichuk, Denys
    Snap, Santa Monica, CA USA..
    Widmaier, Felix
    MPI Tubingen, Tubingen, Germany..
    Wuthrich, Manuel
    MPI Tubingen, Tubingen, Germany..
    Bauer, Stefan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems.
    Handa, Ankur
    Nvidia, Santa Clara, CA 95051 USA..
    Garg, Animesh
    Univ Toronto, Vector Inst, Toronto, ON, Canada.;Nvidia, Santa Clara, CA 95051 USA..
    Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger2022In: 2022 IEEE/RS international conference on intelligent robots and systems (IROS), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 11802-11809Conference paper (Refereed)
    Abstract [en]

    In-hand manipulation of objects is an important capability to enable robots to carry-out tasks which demand high levels of dexterity. This work presents a robot systems approach to learning dexterous manipulation tasks involving moving objects to arbitrary 6-DoF poses. We show empirical benefits, both in simulation and sim-to-real transfer, of using keypoint-based representations for object pose in policy observations and reward calculation to train a model-free reinforcement learning agent. By utilizing domain randomization strategies and large-scale training, we achieve a high success rate of 83% on a real TriFinger system, with a single policy able to perform grasping, ungrasping, and finger gaiting in order to achieve arbitrary poses within the workspace. We demonstrate that our policy can generalise to unseen objects, and success rates can be further improved through finetuning. With the aim of assisting further research in learning in-hand manipulation, we provide a detailed exposition of our system and make the codebase of our system available, along with checkpoints trained on billions of steps of experience, at https://s2r2-ig.github.io

  • 17.
    Almeida, Diogo
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Dual-Arm Robotic Manipulation under Uncertainties and Task-Based Redundancy2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Robotic manipulators are mostly employed in industrial environments, where their tasks can be prescribed with little to no uncertainty. This is possible in scenarios where the deployment time of robot workcells is not prohibitive, such as in the automotive industry. In other contexts, however, the time cost of setting up a classical robotic automation workcell is often prohibitive. This is the case with cellphone manufacturing, for example, which is currently mostly executed by human workers. Robotic automation is nevertheless desirable in these human-centric environments, as a robot can automate the most tedious parts of an assembly. To deploy robots in these environments, however, requires an ability to deal with uncertainties and to robustly execute any given task. In this thesis, we discuss two topics related to autonomous robotic manipulation. First, we address parametric uncertainties in manipulation tasks, such as the location of contacts during the execution of an assembly. We propose and experimentally evaluate two methods that rely on force and torque measurements to produce estimates of task related uncertainties: a method for dexterous manipulation under uncertainties which relies on a compliant rotational degree of freedom at the robot's gripper grasp point and exploits contact  with an external surface, and a cooperative manipulation system which is able to identify the kinematics of a two degrees of freedom mechanism. Then, we consider redundancies in dual-arm robotic manipulation. Dual-armed robots offer a large degree of redundancy which can be exploited to ensure a more robust task execution. When executing an assembly task, for instance, robots can freely change the location of the assembly in their workspace without affecting the task execution. We discuss methods that explore these types of redundancies in relative motion tasks in the form of asymmetries in their execution. Finally, we approach the converse problem by presenting a system which is able to balance measured forces and torques at its end-effectors by leveraging relative motion between them, while grasping a rigid tray. This is achieved through discrete sliding of the grasp points, which constitutes a novel application of bimanual dexterous manipulation.

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  • 18.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caccamo, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Chen, Xi
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    Pinto Basto De Carvalho, Joao F
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Haustein, Joshua
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Vina, Francisco
    KTH.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ögren, Petter
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Team KTH’s Picking Solution for the Amazon Picking Challenge 20162017In: Warehouse Picking Automation Workshop 2017: Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.

  • 19.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ambrus, Rares
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Caccamo, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Chen, Xi
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Pinto Basto de Carvalho, Joao Frederico
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Haustein, Joshua
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Viña, Francisco
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ögren, Petter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Team KTH’s Picking Solution for the Amazon Picking Challenge 20162020In: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment, Springer Nature , 2020, p. 53-62Chapter in book (Other academic)
    Abstract [en]

    In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.

  • 20.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ataer-Cansizoglu, Esra
    Wayfair, Boston, MA 02116, USA.
    Corcodel, Radu
    Mitsubishi Electric Research Labs (MERL), Cambridge, MA 02139, USA.
    Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception2019In: IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2019Conference paper (Refereed)
    Abstract [en]

    We present an interactive perception system that enables an autonomous agent to deliberately interact with its environment and produce 3D object models. Our system verifies object hypotheses through interaction and simultaneously maintains 3D SLAM maps for each rigidly moving object hypothesis in the scene. We rely on depth-based segmentation and a multigroup registration scheme to classify features into various object maps. Our main contribution lies in the employment of a novel segment classification scheme that allows the system to handle incorrect object hypotheses, common in cluttered environments due to touching objects or occlusion. We start with a single map and initiate further object maps based on the outcome of depth segment classification. For each existing map, we select a segment to interact with and execute a manipulation primitive with the goal of disturbing it. If the resulting set of depth segments has at least one segment that did not follow the dominant motion pattern of its respective map, we split the map, thus yielding updated object hypotheses. We show qualitative results with a Fetch manipulator and objects of various shapes, which showcase the viability of the method for identifying and modelling multiple objects through repeated interactions.

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  • 21.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH.
    Karayiannidis, Yiannis
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. Dept. of Electrical Eng., Chalmers University of Technology.
    A Framework for Bimanual Folding Assembly Under Uncertainties2017In: Workshop – Towards robust grasping and manipulation skills for humanoids, 2017Conference paper (Other academic)
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  • 22.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    A Lyapunov-Based Approach to Exploit Asymmetries in Robotic Dual-Arm Task Resolution2019In: 58th IEEE Conference on Decision and Control (CDC), 2019Conference paper (Refereed)
    Abstract [en]

    Dual-arm manipulation tasks can be prescribed to a robotic system in terms of desired absolute and relative motion of the robot’s end-effectors. These can represent, e.g., jointly carrying a rigid object or performing an assembly task. When both types of motion are to be executed concurrently, the symmetric distribution of the relative motion between arms prevents task conflicts. Conversely, an asymmetric solution to the relative motion task will result in conflicts with the absolute task. In this work, we address the problem of designing a control law for the absolute motion task together with updating the distribution of the relative task among arms. Through a set of numerical results, we contrast our approach with the classical symmetric distribution of the relative motion task to illustrate the advantages of our method.

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  • 23.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Asymmetric Dual-Arm Task Execution Using an Extended Relative Jacobian2022In: Robotics Research: 19th International Symposium  ISRR / [ed] Asfour, T Yoshida, E Park, J Christensen, H Khatib, O, Springer Nature , 2022, Vol. 20, p. 18-34Conference paper (Refereed)
    Abstract [en]

    Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetric resolution of relative motion tasks. We discuss how existing approaches enable the asymmetric execution of a relative motion task, and show how an asymmetric relative motion space can be defined. We leverage this result to propose an extended relative Jacobian to model the cooperative system, which allows a user to set a concrete degree of asymmetry in the task execution. This is achieved without the need for prescribing an absolute motion target. Instead, the absolute motion remains available as a functional redundancy to the system. We illustrate the properties of our proposed Jacobian through numerical simulations of a novel differential Inverse Kinematics algorithm.

  • 24.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian2019In: The International Symposium on Robotics Research, 2019Conference paper (Refereed)
    Abstract [en]

    Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetric resolution of relative motion tasks. We discuss how existing approaches enable the asymmetric execution of a relative motion task, and show how an asymmetric relative motion space can be defined. We leverage this result to propose an extended relative Jacobian to model the cooperative system, which allows a user to set a concrete degree of asymmetry in the task execution. This is achieved without the need for prescribing an absolute motion target. Instead, the absolute motion remains available as a functional redundancy to the system. We illustrate the properties of our proposed Jacobian through numerical simulations of a novel differential Inverse Kinematics algorithm.

    Download full text (pdf)
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  • 25.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Dept. of Electrical Eng., Chalmers University of Technology.
    Cooperative Manipulation and Identification of a 2-DOF Articulated Object by a Dual-Arm Robot2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) / [ed] IEEE, 2018, p. 5445-5451Conference paper (Refereed)
    Abstract [en]

    In this work, we address the dual-arm manipula-tion of a two degrees-of-freedom articulated object that consistsof two rigid links. This can include a linkage constrainedalong two motion directions, or two objects in contact, wherethe contact imposes motion constraints. We formulate theproblem as a cooperative task, which allows the employment ofcoordinated task space frameworks, thus enabling redundancyexploitation by adjusting how the task is shared by the robotarms. In addition, we propose a method that can estimate thejoint location and the direction of the degrees-of-freedom, basedon the contact forces and the motion constraints imposed bythe object. Experimental results demonstrate the performanceof the system in its ability to estimate the two degrees of freedomindependently or simultaneously.

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  • 26.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH.
    Karayiannidis, Yiannis
    Chalmers, Sweden.
    Dexterous manipulation by means of compliant grasps and external contacts2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, IEEE, 2017, p. 1913-1920, article id 8206010Conference paper (Refereed)
    Abstract [en]

    We propose a method that allows for dexterousmanipulation of an object by exploiting contact with an externalsurface. The technique requires a compliant grasp, enablingthe motion of the object in the robot hand while allowingfor significant contact forces to be present on the externalsurface. We show that under this type of grasp it is possibleto estimate and control the pose of the object with respect tothe surface, leveraging the trade-off between force control andmanipulative dexterity. The method is independent of the objectgeometry, relying only on the assumptions of type of grasp andthe existence of a contact with a known surface. Furthermore,by adapting the estimated grasp compliance, the method canhandle unmodelled effects. The approach is demonstrated andevaluated with experiments on object pose regulation andpivoting against a rigid surface, where a mechanical springprovides the required compliance.

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  • 27.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Chalmers University of Technology.
    Folding Assembly by Means of Dual-Arm Robotic Manipulation2016In: 2016 IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, p. 3987-3993Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.

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  • 28.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH.
    Karayiannidis, Yiannis
    Robotic Manipulation for Bi-Manual Folding Assembly2015In: Late Breaking Posters, 2015Conference paper (Other academic)
    Abstract [en]

    In this poster the problem of bimanual robotic assembly is considered. In particular we introduce folding assembly which is an assembly task that requires significant rotational motion in order to mate two assembly pieces. We model the connection between the two parts as an ideal virtual prismatic and revolute joint while non-ideal effects on the part movements can be considered as special cases of the ideal virtual joint. The connection between the gripper and the assembly part is also studied by considering the case of rigid and non-rigid grasp. As a proof-of-concept, a stabilizing controller for the assembly task is derived following a bimanual master-slave approach under the assumption of rigid grasps. The controller is validated through simulation while an example object has been designed and printed for experimental validation of our assembly technique.

  • 29.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Viña, Francisco E.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Karayiannidis, Yiannis
    Bimanual Folding Assembly: Switched Control and Contact Point Estimation2016In: IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, 2016, Cancun: IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Robotic assembly in unstructured environments is a challenging task, due to the added uncertainties. These can be mitigated through the employment of assembly systems, which offer a modular approach to the assembly problem via the conjunction of primitives. In this paper, we use a dual-arm manipulator in order to execute a folding assembly primitive. When executing a folding primitive, two parts are brought into rigid contact and posteriorly translated and rotated. A switched controller is employed in order to ensure that the relative motion of the parts follows the desired model, while regulating the contact forces. The control is complemented with an estimator based on a Kalman filter, which tracks the contact point between parts based on force and torque measurements. Experimental results are provided, and the effectiveness of the control and contact point estimation is shown.

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  • 30.
    Almeida, João Tiago
    et al.
    KTH.
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yadollahi, Elmira
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Would you help me?: Linking robot's perspective-taking to human prosocial behavior2023In: HRI 2023: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, p. 388-397Conference paper (Refereed)
    Abstract [en]

    Despite the growing literature on human attitudes toward robots, particularly prosocial behavior, little is known about how robots' perspective-taking, the capacity to perceive and understand the world from other viewpoints, could infuence such attitudes and perceptions of the robot. To make robots and AI more autonomous and self-aware, more researchers have focused on developing cognitive skills such as perspective-taking and theory of mind in robots and AI. The present study investigated whether a robot's perspectivetaking choices could infuence the occurrence and extent of exhibiting prosocial behavior toward the robot.We designed an interaction consisting of a perspective-taking task, where we manipulated how the robot instructs the human to fnd objects by changing its frame of reference and measured the human's exhibition of prosocial behavior toward the robot. In a between-subject study (N=70), we compared the robot's egocentric and addressee-centric instructions against a control condition, where the robot's instructions were object-centric. Participants' prosocial behavior toward the robot was measured using a voluntary data collection session. Our results imply that the occurrence and extent of prosocial behavior toward the robot were signifcantly infuenced by the robot's visuospatial perspective-taking behavior. Furthermore, we observed, through questionnaire responses, that the robot's choice of perspectivetaking could potentially infuence the humans' perspective choices, were they to reciprocate the instructions to the robot.

  • 31.
    Alomari, Muhannad
    et al.
    Univ Leeds, Leeds, W Yorkshire, England..
    Duckworth, Paul
    Univ Leeds, Leeds, W Yorkshire, England..
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Hawasly, Majd
    Univ Leeds, Leeds, W Yorkshire, England..
    Hogg, David C.
    Univ Leeds, Leeds, W Yorkshire, England..
    Cohn, Anthony G.
    Univ Leeds, Leeds, W Yorkshire, England..
    Grounding of human environments and activities for autonomous robots2017In: Proceedings Of The Twenty-Sixth International Joint Conference On Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2017, p. 1395-1402Conference paper (Refereed)
    Abstract [en]

    With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating stateofthe-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-ofconcept, generate simple sentences from templates to describe people and the activities they are engaged in.

  • 32. Althoff, Matthias
    et al.
    Maierhofer, Sebastian
    Pek, Christian
    Provably-Correct and Comfortable Adaptive Cruise Control2020In: IEEE Transactions on Intelligent Vehicles, ISSN 23798858, p. 1-1Article in journal (Refereed)
    Abstract [en]

    Adaptive cruise control is one of the most common comfort features of road vehicles. Despite its large market penetration, current systems are not safe in all driving conditions and require supervision by human drivers. While several previous works have proposed solutions for safe adaptive cruise control, none of these works considers comfort, especially in the event of cut-ins. We provide a novel solution that simultaneously meets our specifications and provides comfort in all driving conditions including cut-ins. This is achieved by an exchangeable nominal controller ensuring comfort combined with a provably correct fail-safe controller that gradually engages an emergency maneuver—this ensures comfort, since most threats are already cleared before emergency braking is fully activated. As a conse- quence, one can easily exchange the nominal controller without having to re-certify the overall system safety. We also provide the first user study for a provably correct adaptive cruise controller. It shows that even though our approach never causes an accident, passengers rate the performance as good as a state-of-the-art solution that does not ensure safety.

  • 33.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Meta-rooms: Building and Maintaining Long Term Spatial Models in a Dynamic World2014In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE conference proceedings, 2014, p. 1854-1861Conference paper (Refereed)
    Abstract [en]

    We present a novel method for re-creating the static structure of cluttered office environments -which we define as the " meta-room" -from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.

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  • 34.
    Andersson, Klas
    a Dept. of Mech. and Astronautical Eng., Naval Postgraduate School, Monterey, CA, United States.
    Extending Endurance for Small UAVs by Predicting and Searching for Thermal Updrafts2009Conference paper (Refereed)
  • 35.
    Andersson, Klas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Improving Fixed Wing UAV Endurance, by Cooperative Autonomous Soaring2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The ever-expanding use and development of smaller UAVs (Unmanned Aerial Vehicles) has highlighted an increasing demand for extended range and endurance for this type of vehicles. 

    In this thesis, the development of a concept and system for autonomous soaring of cooperating unmanned aerial vehicles is presented. The purpose of the developed system is to extend endurance by harvesting energy available in the atmosphere in the form of thermal updrafts, in a similar way that some birds and manned gliders do. By using this “free” energy, considerable improvements in maximum achievable endurance can be realized under a wide variety of atmospherical and weather conditions. 

    The work included theoretical analysis, simulations, and finally flight test- ing of the soaring controller and the system. The system was initially devel- oped as a single-vehicle concept and thereafter extended into a system consist- ing of two cooperating gliders. The purpose of the extension to cooperation, was to further improve the performance of the system by increasing the ability to locate the rising air of thermal updrafts. 

    The theoretical analysis proved the soaring algorithm’s thermal centering controller to be stable. The trials showed the concept of autonomous soaring to function as expected from the simulations. Further it revealed that, by applying the idea, extensive performance gains can be achieved under a fairly wide variety of conditions. 

    The cooperative soaring, likewise, functioned as anticipated and the glid- ers found, cooperated, and climbed together in updrafts. This represents the first and presumably only time cooperative autonomous soaring in this way, has been successfully demonstrated in flight. To draw further conclusions on the advantages of cooperative soaring additional flight trials would, however, be beneficial. 

    Possible issues and limitations were highlighted during the trials and a number of potential improvements were identified. 

    As a part of the work, trials were conducted to verify the viability to implement the system into “real world” operational scenarios. As a proof of concept this was done by tasking the autonomous gliders to perform data/communications relay missions for other UAV systems sending imagery to the ground-station from beyond line of sight (BLOS). The outcome of the trials was positive and the concept appeared to be well suited for these types of missions. The comms relay system was further developed into a hybrid system where the optimal location concerning relay performance was autonomously sought out, after-which the attentiveness then switched to autonomous thermal soaring in the vicinity of this ideal relay position. The hybrid system was tested in simulation and partially flight tested. 

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  • 36.
    Andersson, Klas
    et al.
    KTH, School of Computer Science and Communication (CSC). the Dept. of Mech. & Aeronautical Eng. Naval Postgraduate School, Monterey, CA 93940 USA.
    Jones, Kevin
    Dobrokhodov, Vladimir
    Kaminer, Isaac
    Thermal highs and pitfall lows - notes on the journey to the first cooperative autonomous soaring flight2012In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2012, p. 3392-3397Conference paper (Refereed)
    Abstract [en]

    This paper discusses the development and flight testing of an algorithm for cooperative soaring by multiple autonomous gliders. Flight test results confirmed that the algorithm functioned as expected and that the gliders worked cooperatively to find and utilize the same updrafts during the test. However, the flight also indicated that the effectiveness of the strategy depends largely on the existing thermal conditions in combination with how restrictively the limits of separation be tween the cooperating gliders are set. To the best of the authors' knowledge this was the world's first cooperative autonomous thermal soaring flight.

  • 37.
    Andersson, Klas
    et al.
    Naval Postgraduate School, Monterey, California 93943.
    Kaminer, Isaac
    Naval Postgraduate School, Monterey, California 93943.
    Dobrokhodov, Vladimir
    Naval Postgraduate School, Monterey, California 93943.
    Cichella, Venanzio
    Universita’ di Bologna, 40126 Bologna, Italy.
    Thermal Centering Control for Autonomous Soaring; Stability Analysis and Flight Test Results2012In: Journal of Guidance Control and Dynamics, ISSN 0731-5090, E-ISSN 1533-3884, Vol. 35, no 3, p. 963-975Article in journal (Refereed)
  • 38.
    Andersson, Klas
    et al.
    Naval Postgraduate Shool.
    Kaminer, Issac
    Naval Postgraduate Shool.
    Jones, Kevin
    Naval Postgraduate Shool.
    Dobrokhodov, Vladimir
    Naval Postgraduate Shool.
    Lee, Deok-Jin
    Naval Postgraduate Shool.
    Cooperating UAVs Using Thermal Lift to Extend Endurance2009Conference paper (Refereed)
  • 39. Andersson, O.
    et al.
    Doherty, P.
    Lager, M.
    Lindh, J. -O
    Persson, Linnea
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Topp, E. A.
    Tordenlid, J.
    Wahlberg, Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation2021In: Autonomous Intelligent Systems, ISSN 2730-616X, Vol. 1, no 1, article id 9Article in journal (Refereed)
    Abstract [en]

    A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry. 

  • 40.
    Andreanidis, Christos
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Bergsten, Johanna
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Brümmer, Marcel
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Fröberg, Joel
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Lindestam, Algot
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Persson, Annie
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Pirmohamed, Fahim
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Sandahl, Maria
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Thorapalli Muralidharan, Seshagopalan
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Andrikopoulos, Georgios
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    On the Design and Development of a Tabletop Robot for Interaction with Children2023In: 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1232-1237Conference paper (Refereed)
    Abstract [en]

    This article presents a novel emotionally expressive robot platform targeting social engagement with children. This platform was implemented in accordance with UNICEF's policy guidance on artificial intelligence (AI) for children, focusing on factors such as safety, transparency, reliability and explainability. The robot prototype is presented from a design and development perspective, outlining all utilized electromechanical components that enable its 11 degrees-of-freedom and sensing functions. Preliminary evaluation results are provided in terms of dependability and expressiveness of basic emotions, thus demonstrating the robot's potential to facilitate trustworthy and secure interactions with children.

  • 41.
    Andreasson, Martin
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Undamped Nonlinear Consensus Using Integral Lyapunov Functions2012In: 2012 American Control Conference (ACC), IEEE Computer Society, 2012, p. 6644-6649Conference paper (Refereed)
    Abstract [en]

    This paper analyzes a class of nonlinear consensus algorithms where the input of an agent can be decoupled into a product of a gain function of the agents own state, and a sum of interaction functions of the relative states of its neighbors. We prove the stability of the protocol for both single and double integrator dynamics using novel Lyapunov functions, and provide explicit formulas for the consensus points. The results are demonstrated through simulations of a realistic example within the framework of our proposed consensus algorithm.

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    fulltext
  • 42.
    Andreasson, Martin
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed PI-Control with Applications to Power Systems Frequency Control2014In: American Control Conference (ACC), 2014, IEEE conference proceedings, 2014, p. 3183-3188Conference paper (Refereed)
    Abstract [en]

    This paper considers a distributed PI-controller for networked dynamical systems. Sufficient conditions for when the controller is able to stabilize a general linear system and eliminate static control errors are presented. The proposed controller is applied to frequency control of power transmission systems. Sufficient stability criteria are derived, and it is shown that the controller parameters can always be chosen so that the frequencies in the closed loop converge to nominal operational frequency. We show that the load sharing property of the generators is maintained, i.e., the input power of the generators is proportional to a controller parameter. The controller is evaluated by simulation on the IEEE 30 bus test network, where its effectiveness is demonstrated.

  • 43. Andrikopoulos, G.
    et al.
    Nikolakopoulos, G.
    Manesis, S.
    An experimental study on thermodynamic properties of Pneumatic Artificial Muscles2012In: 2012 20th Mediterranean Conference on Control &amp$\mathsemicolon$ Automation (MED), IEEE , 2012Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 44. Andrikopoulos, George
    Development and Control of a Robotic Climber with Pneumatic Muscle Actuators2010Conference paper (Refereed)
  • 45. Andrikopoulos, George
    et al.
    Arvanitakis, J.
    Manesis, S.
    Nikolakopoulos, G.
    A switched system modeling approach for a Pneumatic Muscle Actuator2012In: 2012 IEEE International Conference on Industrial Technology, IEEE , 2012Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 46. Andrikopoulos, George
    et al.
    Arvanitakis, John
    Nikolakopoulos, George
    Manesis, Stamatis
    Dynamic analysis and cascade movement simulation of a pneumatic muscle actuator2011In: Proceedings of the IASTED International Conference on Modelling, Simulation and Identification, 2011, p. 407-414Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to present a dynamic analysis and a cascade movement simulation of a Pneumatic Muscle Actuator (PMA). PMAs are highly non–linear pneumatic actuators where their elongation are proportional to the interval pressure. Their non–linear characteristics and the property of the hysteresis are posing several difficulties in simulating these pneumatic actuators and to obtain a comprehension of the PMA’s physical movement. In this article a novel detailed modeling, based on hardware in the loop simulationstudies, capable to describe the dynamic characteristic of the PMA and a detailed simulation environment for studying the cascade movement of PMAs will be presented.

    Download full text (pdf)
    fulltext
  • 47. Andrikopoulos, George
    et al.
    Nikolakopoulos, G.
    Manesis, S.
    Non-linear control of Pneumatic Artificial Muscles2013In: 21st Mediterranean Conference on Control and Automation, IEEE , 2013Conference paper (Refereed)
  • 48. Andrikopoulos, George
    et al.
    Nikolakopoulos, George
    Design, development and control of a human-inspired two-arm robot via Pneumatic Artificial Muscles2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, 2017, p. 241-246Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 49. Andrikopoulos, George
    et al.
    Nikolakopoulos, George
    On the design, development and motion control of a HUmanoid Robotic Leg via pneumatic artificial muscles2016In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE , 2016Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 50. Andrikopoulos, George
    et al.
    Nikolakopoulos, George
    Kominiak, Dariusz
    Unander-Scharin, Asa
    Towards the development of a novel upper-body pneumatic humanoid: Design and implementation2016In: 2016 European Control Conference (ECC), IEEE , 2016Conference paper (Refereed)
    Download full text (pdf)
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