拡散モデルに基づく筋骨格ロボットの身体スキーマ学習と異常状態適応
Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots
筋骨格ロボットの異常状態（筋断裂やアクチュエータの固着）でも適応可能な身体スキーマ学習を、拡散モデルを用いた勾配誘導型のデノイジングプロセスとして実現した。シミュレーション実験で有効性を検証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Kento Kawaharazuka, Shuhei Ikemoto
分類: cs.RO
原文アブストラクト
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.
関連論文
- 能動学習による身体スキーマの獲得身体スキーマ学習
- 全身の外受容・自己受容センサからのロボット身体スキーマ学習身体スキーマ学習
