DiAReL: 外乱認識を備えた強化学習によるロボット制御のロバストなSim2Real転移
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
ロボットや環境のモデル化誤差を未知の外乱として扱い、外乱推定を組み込んだ拡張MDPで強化学習を訓練することで、シミュレーションから実機への転移の安定性とロバスト性を高める手法を提案した。
著者: Mohammadhossein Malmir, Josip Josifovski, Noah Klarmann, Alois Knoll
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance on these state augmentations, delay-resolved reinforcement learning algorithms train policies to learn optimal interactions with environments featuring observation or action delays. Although such methods can be directly 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 modeling the robot or environment dynamics as unknown intrinsic disturbances applied to the system input. We introduce the disturbance-augmented Markov decision process (DAMDP) 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 robotic reaching and pushing tasks 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.
