Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robot's workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.

Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems / Saveriano, M.; Lee, D.. - (2019), pp. 112-119. (Intervento presentato al convegno 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 tenutosi a chn nel 2019) [10.1109/IROS40897.2019.8967981].

Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems

Saveriano M.;
2019-01-01

Abstract

Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robot's workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.
2019
IEEE International Conference on Intelligent Robots and Systems
Piscataway, New Jersey, USA
Institute of Electrical and Electronics Engineers Inc.
21530866 21530858
Saveriano, M.; Lee, D.
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems / Saveriano, M.; Lee, D.. - (2019), pp. 112-119. (Intervento presentato al convegno 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 tenutosi a chn nel 2019) [10.1109/IROS40897.2019.8967981].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331047
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