物理的ヒューマンロボットインタラクションにおけるポリシー学習のための生成的シミュレーション
Generative Simulation for Policy Learning in Physical Human-Robot Interaction
大規模言語モデルと視覚言語モデルを用いて、自然言語プロンプトから多様な物理的ヒューマンロボットインタラクションシナリオを自動生成し、模倣学習ポリシーを訓練するフレームワークを提案。ユーザー研究で80%以上の成功率でゼロショットsim-to-real転送を達成した。
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著者: Junxiang Wang, Xinwen Xu, Tiancheng Wu, Julian Millan, Nir Pechuk, Zackory Erickson
分類: cs.RO
原文アブストラクト
Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot behaviors for real-world applications. In this paper, we introduce a zero-shot "text2sim2real" generative simulation framework that automatically synthesizes diverse pHRI scenarios from high-level natural-language prompts. Leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), our pipeline procedurally generates soft-body human models, scene layouts, and robot motion trajectories for assistive tasks. We utilize this framework to autonomously collect large-scale synthetic demonstration datasets and then train vision-based imitation learning policies operating on segmented point clouds. We evaluate our approach through a user study on two physically assistive tasks: scratching and bathing. Our learned policies successfully achieve zero-shot sim-to-real transfer, attaining success rates exceeding 80% and demonstrating resilience to unscripted human motion. Overall, we introduce the first generative simulation pipeline for pHRI applications, automating simulation environment synthesis, data collection, and policy learning. Additional information may be found on our project website: https://rchi-lab.github.io/gen_phri/
