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Article Dans Une Revue IEEE Robotics and Automation Magazine Année : 2021

Reinforcement Learning based, Staircase Negotiation Learning in Simulation and Transfer to Reality for Articulated Tracked Robots

Résumé

Autonomous control of reconfigurable robots is crucial for their deployment in diverse environments. The development of such skills is however hampered by the diversity in hardware and task constraints. We advocate the use of artificial intelligence-based approaches to improve scalability to different conditions and portability to platforms of comparable traversability skills. In particular, we succeed in tackling the problem of staircase traversal via a reinforcement learning-based control framework applicable to different articulated tracked robots, powerful enough to generalize to varying conditions learnt in simulation and to transfer to reality in a zero-shot setting. Our extensive experiments demonstrate the robustness of the framework in learning tasks with increased risk and difficulty induced by platform diversification and increased control dimensionality.
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Dates et versions

hal-03365782 , version 1 (05-10-2021)

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Citer

Andrei Mitriakov, Panagiotis Papadakis, Jérôme Kerdreux, Serge Garlatti. Reinforcement Learning based, Staircase Negotiation Learning in Simulation and Transfer to Reality for Articulated Tracked Robots. IEEE Robotics and Automation Magazine, 2021, 28 (4), pp.10-20. ⟨10.1109/MRA.2021.3114105⟩. ⟨hal-03365782⟩
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