软体机械手通过真实世界试错学会旋转笔，无需预先知道物体属性。
Soft Robotic Dynamic In-Hand Pen Spinning
- 基于真实数据的试错学习，不依赖仿真或物体物理模型。
- 130次采样后对三支不同重量的笔实现100%成功率。
- 可推广至刷子、螺丝刀等异形物体，适合动态抓握研究者。
动态手持操作对软体机器人仍具挑战，尽管其在安全柔顺交互中表现优异，但在高速动态任务中表现不足。本文提出SWIFT系统，通过仅使用真实世界数据，让软体机械手在无先验物体物理信息的情况下，经由试错学习实现笔的旋转。系统利用自标注的真实世界试样，自动发现使软手稳定可靠旋转笔的抓握与旋转基元参数。对三支重量和质心分布不同的笔，每支经过130次采样动作后，成功率达到100%，验证了系统的泛化性与鲁棒性。结果还表明，SWIFT可扩展至不同形状与重量的物体，如刷子（10/10成功）和螺丝刀（5/10成功）。视频、数据与代码已公开于https://soft-spin.github.io。
原文摘要 · Abstract (English)
Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynamic tasks. In this work, we present SWIFT, a system for learning dynamic tasks using a soft and compliant robotic hand. Unlike previous works that rely on simulation, quasi-static actions and precise object models, the proposed system learns to spin a pen through trial-and-error using only real-world data without requiring explicit prior knowledge of the pen's physical attributes. With self-labeled trials sampled from the real world, the system discovers the set of pen grasping and spinning primitive parameters that enables a soft hand to spin a pen robustly and reliably. After 130 sampled actions per object, SWIFT achieves 100% success rate across three pens with different weights and weight distributions, demonstrating the system's generalizability and robustness to changes in object properties. The results highlight the potential for soft robotic end-effectors to perform dynamic tasks including rapid in-hand manipulation. We also demonstrate that SWIFT generalizes to spinning items with different shapes and weights such as a brush and a screwdriver which we spin with 10/10 and 5/10 success rates respectively. Videos, data, and code are available at https://soft-spin.github.io.
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