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Publications (10 of 11) Show all publications
Mitsioni, I., Karayiannidis, Y., Stork, J. A. & Kragic, D. (2019). Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting. In: : . Paper presented at The 2019 IEEE-RAS International Conference on Humanoid Robots, Toronto, Canada, October 15-17, 2019..
Open this publication in new window or tab >>Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Modelling of contact-rich tasks is challenging and cannot be entirely solved using classical control approaches due to the difficulty of constructing an analytic description of the contact dynamics. Additionally, in a manipulation task like food-cutting, purely learning-based methods such as Reinforcement Learning, require either a vast amount of data that is expensive to collect on a real robot, or a highly realistic simulation environment, which is currently not available. This paper presents a data-driven control approach that employs a recurrent neural network to model the dynamics for a Model Predictive Controller. We build upon earlier work limited to torque-controlled robots and redefine it for velocity controlled ones. We incorporate force/torque sensor measurements, reformulate and further extend the control problem formulation. We evaluate the performance on objects used for training, as well as on unknown objects, by means of the cutting rates achieved and demonstrate that the method can efficiently treat different cases with only one dynamic model. Finally we investigate the behavior of the system during force-critical instances of cutting and illustrate its adaptive behavior in difficult cases.

National Category
Engineering and Technology Robotics
Identifiers
urn:nbn:se:kth:diva-258796 (URN)
Conference
The 2019 IEEE-RAS International Conference on Humanoid Robots, Toronto, Canada, October 15-17, 2019.
Note

QC 20191021

Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-10-21Bibliographically approved
Haustein, J. A., Hang, K., Stork, J. A. & Kragic, D. (2019). Object Placement Planning and Optimization for Robot Manipulators. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019): . Paper presented at International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019.
Open this publication in new window or tab >>Object Placement Planning and Optimization for Robot Manipulators
2019 (English)In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

Keywords
Motion planning, Object placing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-264015 (URN)
Conference
International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019
Funder
Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-12-10Bibliographically approved
Hang, K., Lyu, X., Song, H., Stork, J. A., Dollar, A. M., Kragic, D. & Zhang, F. (2019). Perching and resting-A paradigm for UAV maneuvering with modularized landing gears. SCIENCE ROBOTICS, 4(28), Article ID eaau6637.
Open this publication in new window or tab >>Perching and resting-A paradigm for UAV maneuvering with modularized landing gears
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2019 (English)In: SCIENCE ROBOTICS, ISSN 2470-9476, Vol. 4, no 28, article id eaau6637Article in journal (Refereed) Published
Abstract [en]

Perching helps small unmanned aerial vehicles (UAVs) extend their time of operation by saving battery power. However, most strategies for UAV perching require complex maneuvering and rely on specific structures, such as rough walls for attaching or tree branches for grasping. Many strategies to perching neglect the UAV's mission such that saving battery power interrupts the mission. We suggest enabling UAVs with the capability of making and stabilizing contacts with the environment, which will allow the UAV to consume less energy while retaining its altitude, in addition to the perching capability that has been proposed before. This new capability is termed "resting." For this, we propose a modularized and actuated landing gear framework that allows stabilizing the UAV on a wide range of different structures by perching and resting. Modularization allows our framework to adapt to specific structures for resting through rapid prototyping with additive manufacturing. Actuation allows switching between different modes of perching and resting during flight and additionally enables perching by grasping. Our results show that this framework can be used to perform UAV perching and resting on a set of common structures, such as street lights and edges or corners of buildings. We show that the design is effective in reducing power consumption, promotes increased pose stability, and preserves large vision ranges while perching or resting at heights. In addition, we discuss the potential applications facilitated by our design, as well as the potential issues to be addressed for deployment in practice.

Place, publisher, year, edition, pages
AMER ASSOC ADVANCEMENT SCIENCE, 2019
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-251220 (URN)10.1126/scirobotics.aau6637 (DOI)000464024300001 ()2-s2.0-85063677452 (Scopus ID)
Note

QC 20190523

Available from: 2019-05-23 Created: 2019-05-23 Last updated: 2019-05-23Bibliographically approved
Yuan, W., Hang, K., Song, H., Kragic, D., Wang, M. Y. & Stork, J. A. (2019). Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation. In: Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A (Ed.), 2019 International Conference on Robotics and Automation (ICRA): . Paper presented at 2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20-24 May 2019 (pp. 2153-2160). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
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2019 (English)In: 2019 International Conference on Robotics and Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 2153-2160Conference paper, Published paper (Refereed)
Abstract [en]

Moving a human body or a large and bulky object may require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed-rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a robot behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-265483 (URN)10.1109/ICRA.2019.8794160 (DOI)000494942301099 ()2-s2.0-85068443674 (Scopus ID)978-1-5386-6026-3 (ISBN)978-1-5386-6027-0 (ISBN)
Conference
2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20-24 May 2019
Note

QC 20191217

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-09Bibliographically approved
Arnekvist, I., Kragic, D. & Stork, J. A. (2019). Vpe: Variational policy embedding for transfer reinforcement learning. In: 2019 International Conference on Robotics And Automation (ICRA): . Paper presented at 2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20 May 2019 through 24 May 2019 (pp. 36-42). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Vpe: Variational policy embedding for transfer reinforcement learning
2019 (English)In: 2019 International Conference on Robotics And Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 36-42Conference paper, Published paper (Refereed)
Abstract [en]

Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffer from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider the problem of transferring knowledge within a family of similar Markov decision processes. We assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:kth:diva-258072 (URN)10.1109/ICRA.2019.8793556 (DOI)000494942300006 ()2-s2.0-85071508761 (Scopus ID)9781538660263 (ISBN)
Conference
2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20 May 2019 through 24 May 2019
Projects
Factories of the Future (FACT)
Note

QC 20190916

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-12-12Bibliographically approved
Antonova, R., Kokic, M., Stork, J. A. & Kragic, D. (2018). Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation. In: Proceedings of The 2nd Conference on Robot Learning, PMLR 87: . Paper presented at 2nd Conference on Robot Learning, October 29th-31st, 2018, Zürich, Switzerland. (pp. 641-650).
Open this publication in new window or tab >>Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
2018 (English)In: Proceedings of The 2nd Conference on Robot Learning, PMLR 87, 2018, p. 641-650Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.

National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-248396 (URN)
Conference
2nd Conference on Robot Learning, October 29th-31st, 2018, Zürich, Switzerland.
Note

QC 20190507

Available from: 2019-04-07 Created: 2019-04-07 Last updated: 2019-10-28Bibliographically approved
Haustein, J. A., Arnekvist, I., Stork, J. A., Hang, K. & Kragic, D. (2018). Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions. In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018: . Paper presented at Workshop on "Machine Learning in Robot Motion Planning", International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 5 2018.
Open this publication in new window or tab >>Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions
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2018 (English)In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018, 2018Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

n this work we combine sampling-based motionplanning with reinforcement learning and generative modelingto solve non-prehensile rearrangement problems. Our algorithmexplores the composite configuration space of objects and robotas a search over robot actions, forward simulated in a physicsmodel. This search is guided by a generative model thatprovides robot states from which an object can be transportedtowards a desired state, and a learned policy that providescorresponding robot actions. As an efficient generative model,we apply Generative Adversarial Networks.

Keywords
Rearrangement planning, Pushing, Generative Adversarial Models
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-264024 (URN)
Conference
Workshop on "Machine Learning in Robot Motion Planning", International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 5 2018
Note

QCR 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-12-10Bibliographically approved
Yuan, W., Stork, J. A., Kragic, D., Wang, M. Y. & Hang, K. (2018). Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 270-277). IEEE Computer Society
Open this publication in new window or tab >>Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
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2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 270-277Conference paper, Published paper (Refereed)
Abstract [en]

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-237158 (URN)000446394500028 ()2-s2.0-85063133829 (Scopus ID)978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2019-08-20Bibliographically approved
Hang, K., Stork, J. A., Pollard, N. S. & Kragic, D. (2017). A Framework for Optimal Grasp Contact Planning. IEEE Robotics and Automation Letters, 2(2), 704-711
Open this publication in new window or tab >>A Framework for Optimal Grasp Contact Planning
2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 704-711Article in journal (Refereed) Published
Abstract [en]

We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions underwhich minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Grasping, dexterous manipulation, multifingered hands, contact modeling
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-217455 (URN)10.1109/LRA.2017.2651381 (DOI)000413736600043 ()
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-11-17Bibliographically approved
Kokic, M., Stork, J. A., Haustein, J. A. & Kragic, D. (2017). Affordance Detection for Task-Specific Grasping Using Deep Learning. In: 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS): . Paper presented at 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS) (pp. 91-98). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Affordance Detection for Task-Specific Grasping Using Deep Learning
2017 (English)In: 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 91-98Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE-RAS International Conference on Humanoid Robots, ISSN 2164-0572
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-225257 (URN)10.1109/HUMANOIDS.2017.8239542 (DOI)000427350100013 ()2-s2.0-85044473077 (Scopus ID)9781538646786 (ISBN)
Conference
2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS)
Funder
Wallenberg FoundationsSwedish Foundation for Strategic Research Swedish Research Council
Note

QC 20180403

Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2019-12-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3958-6179

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