Proprioceptive-visual correspondence enables self-other distinction in humanoid robots

• URL: http://arxiv.org/abs/2606.13222v1
• Date: Thu, 11 Jun 2026 11:38:34 GMT
• Title: Proprioceptive-visual correspondence enables self-other distinction in humanoid robots
• Abstract summary: Distinguishing self from others is a prerequisite for social intelligence, yet humanoid robots that increasingly share workspaces with humans still lack this ability.<n>We show that a humanoid robot can learn self-other distinction from proprioceptive-visual correspondence, without any identity labels or kinematic models.
• Score: 45.31013373337145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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