ロボティクスにおけるSim2Real転移後の安全な継続的ドメイン適応
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics
ドメインランダム化で事前学習したRL方策を、安全RLと継続学習により実機展開後も安全に適応させ、破滅的忘却を防ぐ手法を提案した論文。
著者: Josip Josifovski, Shangding Gu, Mohammadhossein Malmir, Haoliang Huang, Sayantan Auddy, Nicolás Navarro-Guerrero, Costas Spanos, Alois Knoll
分類: cs.RO, cs.AI
原文アブストラクト
Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications. Many existing domain randomization approaches have been proposed to improve robustness and sim2real transfer. These approaches rely on wide randomization ranges to compensate for the unknown actual system parameters, leading to robust but inefficient real-world policies. In addition, the policies pretrained in the domain-randomized simulation are fixed after deployment due to the inherent instability of the optimization processes based on RL and the necessity of sampling exploitative but potentially unsafe actions on the real system. This limits the adaptability of the deployed policy to the inevitably changing system parameters or environment dynamics over time. We leverage safe RL and continual learning under domain-randomized simulation to address these limitations and enable safe deployment-time policy adaptation in real-world robot control. The experiments show that our method enables the policy to adapt and fit to the current domain distribution and environment dynamics of the real system while minimizing safety risks and avoiding issues like catastrophic forgetting of the general policy found in randomized simulation during the pretraining phase. Videos and supplementary material are available at https://safe-cda.github.io/.
