用优化与学习结合方法，让机械臂轻松接住飞来物体。
CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
- 分阶段规划：先定接球点，再快速到位，最后柔性抓握。
- 仿真成功率98.7%，实测92.6%，冲击力降低28.7%。
- 适合移动机械臂抓取动态物体的场景，开源可复现。
人类能通过缓冲动作轻松接住飞来的物体，但对机器人而言仍是难题。本文提出一种结合优化与学习的柔性抓取框架（CCMM），用于移动机械臂。首先设计高层捕获规划器，计算最优接球位置与关节配置；其次，预抓取规划器使机器人快速抵达目标姿态；为学习柔性抓取策略，提出融合LSTM时序建模与位置编码空间感知的P-LSTM网络，从人类示范中高效学习；最后，后抓取规划器根据P-LSTM输出的柔性序列进行跟踪，并避免因人机结构差异引发的碰撞。在仿真和真实球类捕捉任务中验证，仿真成功率达98.70%，真实测试达92.59%，冲击扭矩降低28.7%。代码已开源，供社区参考。
原文摘要 · Abstract (English)
Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code has be released for the reference of the community.
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