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This is a Experimental Study study.

October 10, 2025Open Access

Robust Visual Embodiment: How Robots Discover Their Bodies in Real Environments

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Systematic analysis quantifies visual degradation impacts on self-modeling in robots, suggesting improvements through advanced denoising techniques.

Robust self-modeling in robots is critical for adaptability in varying environments and conditions.
Visual degradations like blur and noise significantly disrupt accuracy in trajectory planning and damage recovery.
Introducing a task-aware denoising framework shows promising results in preserving morphology cues while isolating robots from clutter.
Extensive tests indicate that this framework restores nearly baseline performance, greatly outpacing existing methods.

Rezvani et al. (2025) studied this question.

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Systematic analysis quantifies visual degradation impacts on self-modeling in robots, suggesting improvements through advanced denoising techniques.