DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control

• URL: http://arxiv.org/abs/2306.09010v1
• Date: Thu, 15 Jun 2023 10:11:38 GMT
• Title: DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
• Authors: Mohammadhossein Malmir (1), Josip Josifovski (1), Noah Klarmann (2), Alois Knoll (1) ((1) Department of Computer Engineering, School of Computation, Information and Technology, Technical University of Munich, (2) Rosenheim University of Applied Sciences)
• Abstract summary: Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance with these state augmentations, delay-resolved reinforcement learning algorithms train policies to learn optimal interactions with environments featured with observation or action delays. Although such methods can directly be trained on the real robots, due to sample inefficiency, limited resources or safety constraints, a common approach is to transfer models trained in simulation to the physical robot. However, robotic simulations rely on approximated models of the physical systems, which hinders the sim2real transfer. In this work, we consider various uncertainties in the modelling of the robot's dynamics as unknown intrinsic disturbances applied on the system input. We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms. The proposed method is validated across several metrics on learning a robotic reaching task and compared with disturbance-unaware baselines. The results show that the disturbance-augmented models can achieve higher stabilization and robustness in the control response, which in turn improves the prospects of successful sim2real transfer.

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