Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots

2026-08-02

AI Summary

This work addresses the challenge of maintaining a consistent body schema in musculoskeletal robots under anomalous conditions such as muscle tears or actuator jams. To this end, the authors propose a diffusion model–based framework for body schema learning that eschews conventional low-dimensional latent space modeling and instead directly operates in the high-dimensional sensor–actuator space. By leveraging gradient-guided denoising, the method estimates physically consistent state variables without requiring retraining, even when faced with out-of-distribution scenarios or partial observations. Physical constraints are explicitly incorporated into the estimation process, ensuring plausibility under perturbations. In simulated musculoskeletal environments, the framework accurately recovers muscle lengths and tensions despite severe disruptions like muscle rupture or actuator lock-up, thereby substantially enhancing the system’s robustness and adaptability.

Abstract

Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes, including abnormalities such as muscle rupture and actuator jamming. Conventional approaches based on autoencoders or variational autoencoders learn average behaviors by projecting sensor and actuator signals into a low-dimensional latent space; however, exploration within the latent space alone has limited capability to handle out-of-distribution or abnormal states that are not included in the training data. To address this limitation, this study proposes a diffusion-based framework for body schema learning in musculoskeletal robots. Unlike generative models that operate through low-dimensional latent spaces, diffusion models can directly and iteratively estimate physically consistent sensor and actuator values in the high-dimensional space through a denoising process, even under partial observations and constraints, without requiring retraining. By formulating body schema adaptation as a gradient-guided denoising process, the proposed method enables adaptive estimation of appropriate muscle lengths and muscle tensions even under abnormal conditions such as muscle rupture and actuator jamming. The validity of the proposed framework is verified through simulation experiments using a musculoskeletal robot model.

Problem

• body schema
• musculoskeletal robots
• abnormal-state adaptation
• out-of-distribution
• sensorimotor consistency

Innovation

• diffusion model
• body schema
• musculoskeletal robot
• abnormal-state adaptation
• gradient-guided denoising