软体机器人动态手持笔旋转

摘要

软机器人系统在安全的柔性交互方面表现出优势，但在高速动态任务方面存在困难，特别是在手部动态操作方面。本文介绍了一种名为SWIFT的系统，用于学习使用软性和柔顺机械手执行动态任务。与先前依赖模拟、准静态动作和精确物体模型的工作不同，所提出的系统通过试错学习旋转笔，仅利用真实世界数据，无需明确先验知识即可了解笔的物理属性。通过从真实世界中采样的自标记试验，系统发现了一组笔抓握和旋转基本参数，使柔性手能够稳健可靠地旋转笔。在每个物体进行了130次采样动作后，SWIFT在三支不同重量和重量分布的笔上实现了100%的成功率，展示了系统对物体属性变化的泛化能力和稳健性。结果突显了软性机器人末端执行器执行动态任务（包括快速手部操作）的潜力。我们还展示了SWIFT可以泛化到旋转具有不同形状和重量的物品，如刷子和螺丝刀，分别以10/10和5/10的成功率旋转。视频、数据和代码可在https://soft-spin.github.io获取。

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.

软体机器人动态手持笔旋转

Soft Robotic Dynamic In-Hand Pen Spinning

摘要

Summary

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