用于意图感知的机器人-人类双手交接的时间触觉编码与柔顺性

中文摘要 AI 辅助

可靠的机器人-人类交接要求机器人推断人类何时准备好接收物体，并在正确时间安全、舒适地释放物体。这一任务具有挑战性，因为仅视觉观察可能无法区分明确的接收意图与意外接触、弱抓取、错误方向的力或瞬时交互。本研究将人机交接视为本质上的多模态问题，方法是将VLA模型与柔顺控制器耦合，该控制器可在物体转移期间降低交互力。我们使用人类演示对VLA模型进行微调，演示数据包含RGB观察、时间编码的触觉反馈以及本体感觉。我们在人体受试者研究中评估完整系统，与两个基准对比：一个无触觉反馈，另一个使用触觉反馈但无柔顺控制。我们假设，将柔顺性与时间触觉编码结合可产生最可靠、最舒适的交接，因为柔顺性促进物理交互，而触觉历史捕捉持续的接收意图。性能通过客观指标和临时问卷测量。结果显示，两个组件提供互补益处，且明显优于基准。代码和数据将在论文接收后发布。

英文摘要

Reliable robot-to-human handover requires the robot to infer when the person is ready to receive the object, and release it safely, comfortably, and at the right time. This is challenging because visual observations alone may not disambiguate clear taking intent from accidental contact, weak grasping, wrong-direction forces, or transient interactions. In this work we treat human-robot handover as an intrinsically multimodal problem. Our approach couples a VLA model with a compliance controller that reduces interaction forces during object transfer. We finetune the VLA model with human demonstrations using RGB observation, temporally encoded tactile feedback and proprioception. We evaluate the complete system in a human-subject study against two baselines: one without tactile feedback and one using tactile feedback without compliance control. We hypothesize that combining compliance and temporal tactile encoding yields the most reliable and comfortable handovers, as compliance facilitates physical interaction while tactile history captures sustained taking intent. Performance is measured through objective metrics and an ad-hoc questionnaire. The results show that the two components provide complementary benefits and substantially outperform the baselines. Code and data will be released upon acceptance.

发表机构

• Istituto Italiano di Tecnologia（意大利技术研究院）
• Università di Genova（热那亚大学）

机构由 AI 辅助整理，请以论文原文为准。