多様な脚ロボットのための体系的なsim-to-real転移：ギャップを埋める
Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
永久磁石同期モータの物理的エネルギー損失モデルを組み込んだsim-to-real強化学習フレームワークを提案し、動的パラメータのランダム化なしに多様な脚ロボットへ方策を転移させ、ANYmalの輸送コストを32%削減した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Filip Bjelonic, Fabian Tischhauser, Marco Hutter
分類: cs.RO
原文アブストラクト
Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches neglect actuator-specific energy losses or depend on complex, hand-tuned reward formulations. We propose a framework that integrates sim-to-real reinforcement learning with a physics-grounded energy model for permanent magnet synchronous motors. The framework requires a minimal parameter set to capture the simulation-to-reality gap and employs a compact four-term reward with a first-principle-based energetic loss formulation that balances electrical and mechanical dissipation. We evaluate and validate the approach through a bottom-up dynamic parameter identification study, spanning actuators, full-robot in-air trajectories and on-ground locomotion. The framework is tested on three primary platforms and deployed on ten additional robots, demonstrating reliable policy transfer without randomization of dynamic parameters. Our method improves energetic efficiency over state-of-the-art methods, achieving a 32 percent reduction in the full Cost of Transport of ANYmal (value 1.27). All code, models, and datasets are publicly available.
