野生環境における四足歩行ロボットのランタイム学習
Runtime Learning of Quadruped Robots in Wild Environments
四足歩行ロボットが動的な野生環境で安全に適応できるよう、強化学習エージェントと検証可能な物理モデルベースの制御器を組み合わせたランタイム学習フレームワークを提案し、シミュレーションで有効性を示した。
著者: Yihao Cai, Yanbing Mao, Lui Sha, Hongpeng Cao, Marco Caccamo
分類: cs.RO
原文アブストラクト
This paper presents a runtime learning framework for quadruped robots, enabling them to learn and adapt safely in dynamic wild environments. The framework integrates sensing, navigation, and control, forming a closed-loop system for the robot. The core novelty of this framework lies in two interactive and complementary components within the control module: the high-performance (HP)-Student and the high-assurance (HA)-Teacher. HP-Student is a deep reinforcement learning (DRL) agent that engages in self-learning and teaching-to-learn to develop a safe and high-performance action policy. HA-Teacher is a simplified yet verifiable physics-model-based controller, with the role of teaching HP-Student about safety while providing a backup for the robot's safe locomotion. HA-Teacher is innovative due to its real-time physics model, real-time action policy, and real-time control goals, all tailored to respond effectively to real-time wild environments, ensuring safety. The framework also includes a coordinator who effectively manages the interaction between HP-Student and HA-Teacher. Experiments involving a Unitree Go2 robot in Nvidia Isaac Gym and comparisons with state-of-the-art safe DRLs demonstrate the effectiveness of the proposed runtime learning framework.
