Proprioceptive-visual correspondence enables self-other distinction in humanoid robots
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 t...
| Authors | Yurun Chen, Tianyuan Gao, Yizhong Ge et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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