生成仿真在物理人机交互中政策学习中的应用
GenPHRI: Agentic Generative Simulation for Physical Human-Robot Interaction
AI总结：
本文提出零样本生成仿真框架，通过自然语言提示生成多样化的物理人机交互场景，用于自主收集合成数据并训练基于视觉的模仿学习策略，实现从仿真到现实的零样本迁移。
摘要：
开发自主的物理人机交互(pHRI)系统受限于大规模训练数据的缺乏，难以学习鲁棒的机器人行为用于实际应用。本文介绍了一个零样本
英文摘要：
Real-world physical human-robot interaction (pHRI) development is costly and slow, while simulation requires substantial task-specific scene and motion authoring. We present GenPHRI, a generative simulation framework for producing deployment-ready pHRI scenarios entirely from natural-language task descriptions using vision-language model (VLM) agents. Generated scenarios contain a room with furniture, a task-appropriate human pose and placement, and a robot executing the requested interaction. Generator and critic agents iterate on each component, and an orchestrator agent controls cross-stage decisions. With GenPHRI, we generate 50 different assistive scenarios in simulation with no manual intervention and deploy two tasks in a 12-participant user study. Vision policies trained on these scenarios achieve zero-shot task completion rates of ~80% in real-world trials against ~90% in simulation, while producing behaviors that participants rate as consistent with textual task prompts. Further, we demonstrate direct execution of generated motion as a complementary, training-free deployment pathway that enables rapid real-world inspection of generated trajectories at the cost of contact coverage. We release GenPHRI as a platform for generating, training, and deploying pHRI tasks from textual prompts. Additional information may be found on our project website: https://rchi-lab.github.io/gen_phri/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
