簡易モデル事前学習とモデルホモトピー転移による脚式ロボットの動的方策学習
Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer
単剛体モデルで方策を事前学習し、質量・慣性を徐々に脚へ再配分する継続的転移で全身動力学へ適応させることで、バク転や壁補助動作などの動的運動を実機四脚ロボットで実現した。
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: Dongyun Kang, Min-Gyu Kim, Tae-Gyu Song, Hajun Kim, Sehoon Ha, Hae-Won Park
分類: cs.RO
原文アブストラクト
Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges. However, the model discrepancy poses a significant challenge when transferring policies to full-body dynamics environments. In this work, we introduce a continuation-based learning framework that combines simplified model pretraining and model-homotopy-inspired transfer to efficiently generate and refine complex dynamic behaviors. First, we pretrain the policy using a single rigid body model to capture core motion patterns in a simplified environment. Next, we employ a continuation strategy to progressively transfer the policy to the full-body environment, minimizing performance loss. To define the continuation path, we introduce a parametric transition path from the single rigid body model to the full-body model by gradually redistributing mass and inertia between the trunk and legs. The proposed method achieves faster convergence and demonstrates superior stability during the transfer process compared to baseline methods. Our framework is validated on a range of dynamic tasks, including flips and wall-assisted maneuvers, and is successfully deployed on a real quadrupedal robot.
