用传感器数据学出软体机器人的运动模型，实现自适应爬行。
Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion
- 从机载传感器推断隐含动力学，作为强化学习的预测模型。
- 仅用惯性与测距传感器，在仿真中学会有效爬行策略。
- 适合研究软体机器人自适应运动控制的学者参考。
软体爬行机器人利用柔性身体变形和顺应性通过表面接触实现移动。由于模型不准确、传感器噪声以及需发现运动步态，设计其控制策略极具挑战。本文提出一种基于隐含动力学的模型化强化学习框架，利用机载传感器推断的隐状态作为预测模型，指导演员-评论家算法优化运动策略。在模拟环境中，使用惯性测量单元（IMU）和时间飞行（ToF）传感器作为观测输入，所学隐含动力学可实现短时程运动预测，而演员-评论家算法则发现有效运动策略。该方法展示了仅依赖噪声传感器反馈，通过隐含动力学建模实现具身软体机器人自适应运动的潜力。
原文摘要 · Abstract (English)
Soft robotic crawlers are mobile robots that utilize soft body deformability and compliance to achieve locomotion through surface contact. Designing control strategies for such systems is challenging due to model inaccuracies, sensor noise, and the need to discover locomotor gaits. In this work, we present a model-based reinforcement learning (MB-RL) framework in which latent dynamics inferred from onboard sensors serve as a predictive model that guides an actor-critic algorithm to optimize locomotor policies. We evaluate the framework on a minimal crawler model in simulation using inertial measurement units and time-of-flight sensors as observations. The learned latent dynamics enable short-horizon motion prediction while the actor-critic discovers effective locomotor policies. This approach highlights the potential of latent-dynamics MB-RL for enabling embodied soft robotic adaptive locomotion based solely on noisy sensor feedback.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。