机器人通过身体感知与视觉对应学会区分自己和他人。
Proprioceptive-visual correspondence enables self-other distinction in humanoid robots
- 利用本体感觉与视觉信号的对应关系实现自我识别。
- 无需标签或运动模型，可构建三维自体模型并预测动作结果。
- 适合需要与人共处的协作型机器人研究者参考。
区分自我与他人是社交智能的基础，但日益与人类共用工作空间的人形机器人仍缺乏此能力。本文展示，人形机器人可通过本体感觉-视觉对应关系学习自我-他人区分，无需身份标签或运动学模型。一旦建立该区分，系统可自举生成一个预测性自体模型，将关节配置映射到三维体占据空间，捕捉动作对自身身体形态的影响。在包含人类或形态完全相同的机器人等多智能体场景中，系统能可靠识别自身，学习三维自体模型，并支持目标抓取、避障运动规划及人到机器人的动作迁移等下游任务。这些成果为机器人在共享物理环境中实现身体自我表征提供了可行路径。项目页面：https://euron-zc.github.io/humanoid-self-model/
原文摘要 · Abstract (English)
Distinguishing self from others is a prerequisite for social intelligence, yet humanoid robots that increasingly share workspaces with humans still lack this ability. Here we show that a humanoid robot can learn self-other distinction from proprioceptive-visual correspondence, without any identity labels or kinematic models. Once established, this distinction bootstraps a predictive self-model that maps joint configurations to three-dimensional body occupancy, capturing how the robot's body changes with action. In multi-agent scenes involving humans or morphologically identical robots, the system reliably identifies itself, learns a 3D self-model, and supports downstream tasks including target reaching, collision-aware motion planning, and human-to-robot motion retargeting. Together, these results outline a route toward bodily self-representation in robots that act and coordinate alongside others in shared physical environments. Project page: https://euron-zc.github.io/humanoid-self-model/.
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