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

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: this https URL.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2606.13222 [cs.RO]

(or arXiv:2606.13222v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.13222 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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