MimicAgent: プロンプトから軌道生成による四足歩行スキルの獲得
MimicAgent: Quadruped Skills via Prompt-to-Trajectory Generation
スキルを表すプロンプトからコーディングエージェントで四足ロボットの参照軌道を生成し、それを用いた模倣強化学習で実機展開可能な動的スキルを学習するフレームワークを提案。
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著者: Lucky Kant Nayak, Narayanan Palghat Parameswaran, Neehar Peri, Deva Ramanan
分類: cs.RO
原文アブストラクト
We present MimicAgent, a prompt-to-trajectory generation framework for learning dynamic quadruped skills. Although reward shaping is extensively used when training quadruped policies, navigating the resulting reward landscape is notoriously difficult, requiring hours of "graduate student descent". Eureka attempts to automate reward design with LLMs, but we find that it struggles to generalize across diverse skills and morphologies. Our key observation is that it is far easier for a human - and by association, an LLM - to generate reference motions than to shape reward functions. Our hypothesis is motivated by the success of example-guided RL for humanoids, which exploits large-scale motion capture datasets as references for training locomotion policies. Unlike humanoids, quadrupeds lack such reference motion data. Towards this end, we propose MimicAgent, an agentic harness that, given a skill prompt, generates quadruped reference trajectories with coding agents. These coarse reference trajectories are then used to train example-guided RL policies that are deployable in simulation and in the real-world. Notably, we find that when prompting Claude Fable 5.1 within our agentic harness, 87% of prompts yield semantically aligned reference trajectories.