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Khanna, P., Fredberg, J., Björkman, M., Smith, C. & Linard, A. (2024). Hand it to me formally! Data-driven control for human-robot handovers with signal temporal logic. IEEE Robotics and Automation Letters, 9(10), 9039-9046
Open this publication in new window or tab >>Hand it to me formally! Data-driven control for human-robot handovers with signal temporal logic
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2024 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 9, no 10, p. 9039-9046Article in journal (Refereed) Published
Abstract [en]

To facilitate human-robot interaction (HRI), we aim for robot behavior that is efficient, transparent, and closely resembles human actions. Signal Temporal Logic (STL) is a formal language that enables the specification and verification of complex temporal properties in robotic systems, helping to ensure their correctness. STL can be used to generate explainable robot behaviour, the degree of satisfaction of which can be quantified by checking its STL robustness. In this letter, we use data-driven STL inference techniques to model human behavior in human-human interactions, on a handover dataset. We then use the learned model to generate robot behavior in human-robot interactions. We present a handover planner based on inferred STL specifications to command robotic motion in human-robot handovers. We also validate our method in a human-to-robot handover experiment.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Handover, Robots, Robot kinematics, Behavioral sciences, Trajectory, Logic, Robustness, Human-robot handovers, Signal Temporal Logic (STL)
National Category
Robotics and automation Control Engineering
Identifiers
urn:nbn:se:kth:diva-354524 (URN)10.1109/LRA.2024.3447476 (DOI)001316210300020 ()2-s2.0-85201769650 (Scopus ID)
Note

QC 20241011

Available from: 2024-10-11 Created: 2024-10-11 Last updated: 2025-02-05Bibliographically approved
Linard, A., Gautier, A., Duberg, D. & Tumova, J. (2024). Robust MITL planning under uncertain navigation times. In: 2024 IEEE International Conference on Robotics and Automation, ICRA 2024: . Paper presented at 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, May 13-17, 2024, Yokohama, Japan (pp. 2498-2504). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Robust MITL planning under uncertain navigation times
2024 (English)In: 2024 IEEE International Conference on Robotics and Automation, ICRA 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 2498-2504Conference paper, Published paper (Refereed)
Abstract [en]

In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL)-a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a strategy that maximizes the temporal robustness of the robot's MITL task. As the first step towards a solution, we define a Mixed-integer linear programming approach to solving the task planning problem over a Varying Weighted Transition System, where navigation durations are deterministic but vary depending on the time of day. Then, we apply this planner to optimize for MITL temporal robustness in Markov Decision Processes, where the navigation durations between physical locations are uncertain, but the time-dependent distribution over possible delays is known. Finally, we develop a receding horizon planner for Markov Decision Processes that preserves guarantees over MITL temporal robustness. We show the scalability of our planning algorithms in simulations of robotic tasks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Formal Methods, Markov Decision Processes, Planning Under Uncertainty, Temporal Robustness
National Category
Computer Sciences Control Engineering
Identifiers
urn:nbn:se:kth:diva-353565 (URN)10.1109/ICRA57147.2024.10611704 (DOI)2-s2.0-85202431026 (Scopus ID)
Conference
2024 IEEE International Conference on Robotics and Automation, ICRA 2024, May 13-17, 2024, Yokohama, Japan
Note

Part of ISBN: 9798350384574

QC 20240926

Available from: 2024-09-19 Created: 2024-09-19 Last updated: 2024-09-26Bibliographically approved
Linard, A., Torre, I., Bartoli, E., Sleat, A., Leite, I. & Tumova, J. (2023). Real-time RRT* with Signal Temporal Logic Preferences. In: 2023 IEEE/RSJ international conference on intelligent robots and systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 1-5, 2023, Detroit, USA. IEEE
Open this publication in new window or tab >>Real-time RRT* with Signal Temporal Logic Preferences
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2023 (English)In: 2023 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, 2023Conference paper, Published paper (Other academic)
Abstract [en]

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatiotemporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly ExploringRandom Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Signal Temporal Logic, Real-Time Planning, Sampling-based Motion Planning.
National Category
Control Engineering Computer Engineering
Identifiers
urn:nbn:se:kth:diva-325105 (URN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 1-5, 2023, Detroit, USA
Note

QC 20231122

Available from: 2023-03-29 Created: 2023-03-29 Last updated: 2023-11-22Bibliographically approved
Linard, A., Torre, I., Bartoli, E., Sleat, A., Leite, I. & Tumova, J. (2023). Real-Time RRT* with Signal Temporal Logic Preferences. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023: . Paper presented at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Detroit, United States of America, Oct 1 2023 - Oct 5 2023 (pp. 8621-8627). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Real-Time RRT* with Signal Temporal Logic Preferences
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2023 (English)In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 8621-8627Conference paper, Published paper (Refereed)
Abstract [en]

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly Exploring Random Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Real-Time Planning, Sampling-based Motion Planning, Signal Temporal Logic
National Category
Robotics and automation
Identifiers
urn:nbn:se:kth:diva-350253 (URN)10.1109/IROS55552.2023.10341993 (DOI)001136907802112 ()2-s2.0-85177884865 (Scopus ID)
Conference
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Detroit, United States of America, Oct 1 2023 - Oct 5 2023
Note

Part of ISBN 9781665491907

QC 20240710

Available from: 2024-07-10 Created: 2024-07-10 Last updated: 2025-02-09Bibliographically approved
Linard, A., Torre, I., Leite, I. & Tumova, J. (2022). Inference of Multi-Class STL Specifications for Multi-Label Human-Robot Encounters. In: 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 23-27, 2022, Kyoto, JAPAN (pp. 1305-1311). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Inference of Multi-Class STL Specifications for Multi-Label Human-Robot Encounters
2022 (English)In: 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1305-1311Conference paper, Published paper (Refereed)
Abstract [en]

This paper is interested in formalizing human trajectories in human-robot encounters. Inspired by robot navigation tasks in human-crowded environments, we consider the case where a human and a robot walk towards each other, and where humans have to avoid colliding with the incoming robot. Further, humans may describe different behaviors, ranging from being in a hurry/minimizing completion time to maximizing safety. We propose a decision tree-based algorithm to extract STL formulae from multi-label data. Our inference algorithm learns STL specifications from data containing multiple classes, where instances can be labelled by one or many classes. We base our evaluation on a dataset of trajectories collected through an online study reproducing human-robot encounters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keywords
Temporal Logic Inference, Signal Temporal Logic, Human-Robot Interaction
National Category
Robotics and automation
Identifiers
urn:nbn:se:kth:diva-324993 (URN)10.1109/IROS47612.2022.9982088 (DOI)000908368201044 ()2-s2.0-85146319785 (Scopus ID)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 23-27, 2022, Kyoto, JAPAN
Note

QC 20230327

Available from: 2023-03-27 Created: 2023-03-27 Last updated: 2025-02-09Bibliographically approved
Linard, A., Torre, I., Steen, A., Leite, I. & Tumova, J. (2021). Formalizing Trajectories in Human-Robot Encounters via Probabilistic STL Inference. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 27-OCT 01, 2021, ELECTR NETWORK, Prague. (pp. 9857-9862). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Formalizing Trajectories in Human-Robot Encounters via Probabilistic STL Inference
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2021 (English)In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 9857-9862Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we are interested in formalizing human trajectories in human-robot encounters. We consider a particular case where a human and a robot walk towards each other. A question that arises is whether, when, and how humans will deviate from their trajectory to avoid a collision. These human trajectories can then be used to generate socially acceptable robot trajectories. To model these trajectories, we propose a data-driven algorithm to extract a formal specification expressed in Signal Temporal Logic with probabilistic predicates. We evaluated our method on trajectories collected through an online study where participants had to avoid colliding with a robot in a shared environment. Further, we demonstrate that probabilistic STL is a suitable formalism to depict human behavior, choices and preferences in specific scenarios of social navigation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keywords
Temporal Logic Inference, Signal Temporal Logic, Human-Robot Interaction
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-310027 (URN)10.1109/IROS51168.2021.9635951 (DOI)000755125507092 ()2-s2.0-85122903550 (Scopus ID)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 27-OCT 01, 2021, ELECTR NETWORK, Prague.
Note

QC 20220323

Part of proceedings: ISBN 978-1-6654-1714-3

Available from: 2022-03-23 Created: 2022-03-23 Last updated: 2022-06-25Bibliographically approved
Linard, A. & Tumova, J. (2020). Active Learning of Signal Temporal Logic Specifications. In: IEEE International Conference on Automation Science and Engineering: . Paper presented at 16th IEEE International Conference on Automation Science and Engineering, CASE 2020, AUG 20-21, 2020, ELECTR NETWORK (pp. 779-785). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Active Learning of Signal Temporal Logic Specifications
2020 (English)In: IEEE International Conference on Automation Science and Engineering, Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 779-785Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a method to infer temporal logic behaviour models of an a priori unknown system. We use the formalism of Signal Temporal Logic (STL), which can express various robot motion planning and control specifications, including spatial preferences. In our setting, data is collected through a series of queries the learning algorithm poses to the system under test. This active learning approach incrementally builds a hypothesis solution which, over time, converges to the actual behaviour of the system. Active learning presents several benefits compared to supervised learning: in the case of costly prior labelling of data, and if the system to test is accessible, the learning algorithm can interact with the system to refine its guess of the specification of the system. Inspired by mobile robot navigation tasks, we present experimental case studies to ensure the relevance of our method.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Series
IEEE International Conference on Automation Science and Engineering, ISSN 2161-8070
Keywords
Computer circuits, Learning systems, Mobile robots, Robot programming, Specifications, Temporal logic, Active Learning, Behaviour models, Case-studies, Mobile Robot Navigation, Robot motion planning, System under test, Temporal logic specifications, Learning algorithms
National Category
Robotics and automation Control Engineering
Identifiers
urn:nbn:se:kth:diva-291610 (URN)10.1109/CASE48305.2020.9216778 (DOI)000612200600111 ()2-s2.0-85094134224 (Scopus ID)
Conference
16th IEEE International Conference on Automation Science and Engineering, CASE 2020, AUG 20-21, 2020, ELECTR NETWORK
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Research Council
Note

QC 20210319

Available from: 2021-03-19 Created: 2021-03-19 Last updated: 2025-02-05Bibliographically approved
Linard, A., Bucur, D. & Stoelinga, M. (2019). Fault trees from data: Efficient learning with an evolutionary algorithm. In: 5th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2019: . Paper presented at 5th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2019, 27 November 2019 through 29 November 2019 (pp. 19-37). Springer
Open this publication in new window or tab >>Fault trees from data: Efficient learning with an evolutionary algorithm
2019 (English)In: 5th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2019, Springer , 2019, p. 19-37Conference paper, Published paper (Refereed)
Abstract [en]

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components’ failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Cyber-physical systems, Evolutionary algorithm, Fault tree induction, Safety-critical systems, Application programs, Cyber Physical System, Embedded systems, Evolutionary algorithms, Iterative methods, Learning algorithms, Population statistics, Reliability, Safety engineering, Complex architectures, Effective algorithms, Efficient learning, Fault tree structures, Fault-trees, Large amounts of data, Safety critical systems, System reliability models, Trees (mathematics)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-268580 (URN)10.1007/978-3-030-35540-1_2 (DOI)2-s2.0-85076704235 (Scopus ID)
Conference
5th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2019, 27 November 2019 through 29 November 2019
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20200506

Part of ISBN 9783030355395

Available from: 2020-05-06 Created: 2020-05-06 Last updated: 2024-10-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7258-1527

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