发表机构
National University of Singapore; City University of Hong Kong（新加坡国立大学; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出具身快照控制器，通过分离慢速预加载与快速弹性释放，使软体臂超越腱驱动速度限制，实现高效触达与附着，实验验证了其有效性与实用性。
AI 中文摘要
带有被动吸盘的软体机械臂的触达与附着需要精确的目标定位和足够的接触速度，然而齿轮传动执行器可能施加速度限制，仅靠改进轨迹跟踪无法克服。本文提出了一种具身快照控制器，将慢速伺服驱动的预加载与快速弹性释放分离，使柔顺臂能够超越其直接腱驱动的速度限制。章鱼的生物学特性启发了该控制器的分节组织先验，而非对章鱼神经系统的复现。一个在三个节段间共享的学习策略选择预加载、瞄准、腱松弛和释放时机，决定在何处、如何以及何时加载和释放身体。该策略使用硬件验证的循环模型在实验支持的边界内进行离线优化。在五个优化种子和400个未见过的模拟目标中，附着成功率为$(73\pm4)\\%$，横向容差为$5\text{ cm}$，共享策略在共同评估预算的中位数$17\\%$后达到匹配的集中式控制器的平均最终奖励。硬件表征实现了1.56-1.64 m/s的末端速度，至少比直接腱驱动释放高$108\\%$。在六个放置位置的18次开环硬件试验中，17次超过1 m/s的快照阈值，九次成功取回物体，其中五个放置位置成功取回。这些结果展示了控制问题中责任的实用分工：学习控制准备身体，被动身体力学执行动态触达与附着所需的快速运动。
英文摘要
Reach-and-attach of soft robotic arms with passive suction requires accurate targeting and sufficient contact speed, yet geared actuators can impose a speed limit that improved trajectory tracking alone cannot overcome. This paper proposes an embodied snap controller that separates slow servo-driven preloading from rapid elastic release, enabling a compliant arm to move beyond its direct tendon-driven speed limit. Octopus biology motivates the controller's section-wise organizational prior, rather than reproduction of the octopus nervous system. A learned policy shared across three sections selects preloads, aim, tendon slack, and release timing, determining where, how, and when to load and release the body. The policy is optimized offline using a hardware-validated recurrent model within experimentally supported bounds. Across five optimization seeds and 400 unseen simulated targets, attachment success is $(73\pm4)\%$ at a $5\text{ cm}$ lateral tolerance, and the shared policy reaches the matched centralized controller's mean final reward after a median $17\%$ of the common evaluation budget. Hardware characterization achieves tip speeds of 1.56-1.64 m/s, at least $108\%$ above direct tendon-driven release. In 18 open-loop hardware trials across six placements, 17 exceed the 1 m/s snap threshold and nine retrieve the object, with successful retrieval at five placements. These results demonstrate a practical division of responsibility in the control problem: learned control prepares the body, and passive body mechanics execute the rapid movement needed for dynamic reach-and-attach.
