基于扩散模型的身体模式学习：实现肌肉骨骼机器人的异常状态适应
Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots
- The University of Tokyo（东京大学）
- Kyushu Institute of Technology（九州工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对肌肉骨骼机器人传统身体模式学习方法难以处理异常状态的问题，提出基于扩散模型的身体模式学习框架，通过仿真实验验证了其异常状态下自适应估计肌肉参数的有效性。
AI中文摘要：
肌肉骨骼机器人需要内部身体模式，该模式需在广泛的物理状态变化（包括肌肉破裂、执行器卡滞等异常情况）下保持一致。基于自动编码器或变分自动编码器的传统方法，通过将传感器与执行器信号投影到低维潜在空间来学习平均行为；但仅在潜在空间内探索，处理训练数据中未包含的分布外或异常状态的能力有限。为解决此局限，本研究提出一种用于肌肉骨骼机器人身体模式学习的扩散框架。与通过低维潜在空间运行的生成模型不同，扩散模型可在高维空间中，通过去噪过程直接迭代估计物理一致的传感器与执行器值，即便在部分观测与约束下也无需重新训练。通过将身体模式适应建模为梯度引导的去噪过程，所提方法可在肌肉破裂、执行器卡滞等异常条件下，自适应估计合适的肌肉长度与肌肉张力。采用肌肉骨骼机器人模型的仿真实验验证了该框架的有效性。
英文摘要：
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.
