# Egocentric Visual Self-Modeling for Autonomous Robot Dynamics Prediction and Adaptation

arXiv: https://arxiv.org/abs/2207.03386 · HTML: https://mlsanity.com/paper/2207.03386/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2022.

## Built on by (most cited first, 5 of 5)

- [Differentiable Robot Rendering](https://mlsanity.com/paper/2410.13851/) (2024)
- [EgoX: Egocentric Video Generation from a Single Exocentric Video](https://mlsanity.com/paper/2512.08269/) (2025)
- [ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models](https://mlsanity.com/paper/2408.15919/) (2024)
- [Proprioceptive-visual correspondence enables self-other distinction in humanoid robots](https://mlsanity.com/paper/2606.13222/) (2026)
- [Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms](https://mlsanity.com/paper/2407.15283/) (2024)

## Abstract (arXiv)

The ability of robots to model their own dynamics is key to autonomous planning and learning, as well as for autonomous damage detection and recovery. Traditionally, dynamic models are pre-programmed or learned from external observations. Here, we demonstrate for the first time how a task-agnostic dynamic self-model can be learned using only a single first-person-view camera in a self-supervised manner, without any prior knowledge of robot morphology, kinematics, or task. Through experiments on a 12-DoF robot, we demonstrate the capabilities of the model in basic locomotion tasks using visual input. Notably, the robot can autonomously detect anomalies, such as damaged components, and adapt its behavior, showcasing resilience in dynamic environments. Furthermore, the model's generalizability was validated across robots with different configurations, emphasizing its potential as a universal tool for diverse robotic systems. The egocentric visual self-model proposed in our work paves the way for more autonomous, adaptable, and resilient robotic systems.