用扩散模型让机器人自适应肌肉损伤等异常状态。
Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots
- 基于扩散模型直接在高维空间迭代修复传感器与执行器数据。
- 无需重训练即可在肌肉断裂或电机卡死时估计合理肌长与张力。
- 适合需要强鲁棒性的仿生机器人控制场景。
肌骨骼机器人需具备在多种物理状态变化下保持一致的内部身体表征，包括肌肉断裂、执行器卡死等异常情况。传统基于自编码器或变分自编码器的方法通过将传感器与执行器信号映射到低维潜在空间来学习平均行为，但仅在潜在空间中探索难以处理未包含在训练数据中的分布外或异常状态。为此，本文提出一种基于扩散模型的肌骨骼机器人身体表征学习框架。不同于在低维潜在空间运行的生成模型，扩散模型可通过去噪过程在高维空间中直接、迭代地估计物理上一致的传感器与执行器值，即使在部分观测和约束条件下也无需重训练。通过将身体表征适应建模为梯度引导的去噪过程，该方法能在肌肉断裂或执行器卡死等异常条件下实现对合适肌长与肌张力的自适应估计。所提框架的有效性通过使用肌骨骼机器人模型的仿真实验得到验证。
原文摘要 · Abstract (English)
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.
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