柔性機器人動態手持筆旋轉

摘要

在軟性機器人系統中，動態手部操作仍然是一個具有挑戰性的任務，這些系統在安全柔軟的互動方面具有優勢，但在高速動態任務方面卻遇到困難。在這項工作中，我們提出了一個名為SWIFT的系統，用於學習使用軟性和柔軟的機器人手進行動態任務。與先前依賴模擬、準靜態動作和精確物體模型的作品不同，所提出的系統通過僅使用現實世界數據進行試錯學習來學習旋轉筆的技能，而無需明確事先了解筆的物理特性。通過從現實世界中採樣的自標記試驗，系統發現了一組筆抓取和旋轉基本參數，使得柔性手部能夠穩健可靠地旋轉筆。在對每個物體進行130次採樣動作後，SWIFT在三支具有不同重量和重量分佈的筆上實現了100%的成功率，展示了系統對於物體特性變化的泛化能力和穩健性。結果突顯了軟性機器人末端執行器執行動態任務，包括快速手部操作的潛力。我們還展示了SWIFT能夠泛化到旋轉不同形狀和重量物品，如刷子和螺絲刀，其成功率分別為10/10和5/10。視頻、數據和代碼可在https://soft-spin.github.io獲得。

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.

柔性機器人動態手持筆旋轉

Soft Robotic Dynamic In-Hand Pen Spinning

摘要

Summary

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