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

arXiv: https://arxiv.org/abs/2606.13222 · HTML: https://mlsanity.com/paper/2606.13222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking](https://mlsanity.com/paper/2510.02252/) (2025)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2501.09782/) (2025)
- [Robot self/other distinction: active inference meets neural networks learning in a mirror](https://mlsanity.com/paper/2004.05473/) (2020)
- [Teaching Robots to Build Simulations of Themselves](https://mlsanity.com/paper/2311.12151/) (2023)
- [Egocentric Visual Self-Modeling for Autonomous Robot Dynamics Prediction and Adaptation](https://mlsanity.com/paper/2207.03386/) (2022)
- [High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning](https://mlsanity.com/paper/2310.03624/) (2023)

## Built on by (most cited first, 1 of 1)

- [Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement](https://mlsanity.com/paper/2609.13236/) (2026)

## Abstract (arXiv)

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/.