# Robot self/other distinction: active inference meets neural networks learning in a mirror

arXiv: https://arxiv.org/abs/2004.05473 · HTML: https://mlsanity.com/paper/2004.05473/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Multimodal VAE Active Inference Controller](https://mlsanity.com/paper/2103.04412/) (2021)
- [Know Thyself? On the Incapability and Implications of AI Self-Recognition](https://mlsanity.com/paper/2510.03399/) (2025)
- [Adaptation through prediction: multisensory active inference torque control](https://mlsanity.com/paper/2112.06752/) (2021)
- [Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception](https://mlsanity.com/paper/2105.04261/) (2021)
- [Deconstructing deep active inference](https://mlsanity.com/paper/2303.01618/) (2023)
- [Survey and Perspective on Social Emotions in Robotics](https://mlsanity.com/paper/2105.09647/) (2021)
- [Structure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning](https://mlsanity.com/paper/2411.11511/) (2024)
- [Proprioceptive-visual correspondence enables self-other distinction in humanoid robots](https://mlsanity.com/paper/2606.13222/) (2026)
- [Dynamic planning in hierarchical active inference](https://mlsanity.com/paper/2402.11658/) (2024)
- [Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL](https://mlsanity.com/paper/2407.01570/) (2024)
- [Active Inference with a Self-Prior in the Mirror-Mark Task](https://mlsanity.com/paper/2604.09673/) (2026)

## Abstract (arXiv)

Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware. However, only a selected group of animals, mainly high order mammals such as humans, has passed the mirror test, a behavioural experiment proposed to assess self-recognition abilities. In this paper, we describe self-recognition as a process that is built on top of body perception unconscious mechanisms. We present an algorithm that enables a robot to perform non-appearance self-recognition on a mirror and distinguish its simple actions from other entities, by answering the following question: am I generating these sensations? The algorithm combines active inference, a theoretical model of perception and action in the brain, with neural network learning. The robot learns the relation between its actions and its body with the effect produced in the visual field and its body sensors. The prediction error generated between the models and the real observations during the interaction is used to infer the body configuration through free energy minimization and to accumulate evidence for recognizing its body. Experimental results on a humanoid robot show the reliability of the algorithm for different initial conditions, such as mirror recognition in any perspective, robot-robot distinction and human-robot differentiation.