用柔软机械手的自感知特性，让人直接教机器人抓东西
KineSoft: Learning Proprioceptive Manipulation Policies with Soft Robot Hands
- 用内置应变传感器获取手部形变数据，实现无遮挡自感知
- 通过人体物理引导，学习到更精准的抓取动作策略
- 适合想快速训练软体机械手的工程师和研究者
欠驱动的软体机械手相比刚性系统具有天然的安全性和适应性优势，但发展灵巧操作能力仍具挑战。尽管模仿学习在复杂操作任务中展现出潜力，传统方法因示范采集困难及状态表示无效，难以应用于软体系统。我们提出KineSoft框架，利用软体手的自然柔顺性作为技能教学优势，而非仅视为控制难题。该框架包含两项关键贡献：（1）一种内部应变传感阵列，实现无遮挡的本体感觉形变估计；（2）基于形变的模仿学习框架，结合低层形变条件控制器，将扩散模型策略与本体感觉反馈相耦合。这使得人类示范者可直接物理引导机器人，系统则学习将本体感觉模式与成功操作策略相关联。我们在真实实验中验证了该方法，结果显示其形变估计精度优于基线方法，能够精确跟踪形变轨迹，并在任务成功率上显著高于传统模仿学习方法。
原文摘要 · Abstract (English)
Underactuated soft robot hands offer inherent safety and adaptability advantages over rigid systems, but developing dexterous manipulation skills remains challenging. While imitation learning shows promise for complex manipulation tasks, traditional approaches struggle with soft systems due to demonstration collection challenges and ineffective state representations. We present KineSoft, a framework enabling direct kinesthetic teaching of soft robotic hands by leveraging their natural compliance as a skill teaching advantage rather than only as a control challenge. KineSoft makes two key contributions: (1) an internal strain sensing array providing occlusion-free proprioceptive shape estimation, and (2) a shape-based imitation learning framework that uses proprioceptive feedback with a low-level shape-conditioned controller to ground diffusion-based policies. This enables human demonstrators to physically guide the robot while the system learns to associate proprioceptive patterns with successful manipulation strategies. We validate KineSoft through physical experiments, demonstrating superior shape estimation accuracy compared to baseline methods, precise shape-trajectory tracking, and higher task success rates compared to baseline imitation learning approaches.
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