高效规划多接触操作轨迹，兼顾精度与计算速度。
Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations
- 分层优化：先用MILP选最优接触点，再用NLP精调轨迹
- 通过二进制编码松弛双线性约束，提升求解紧致性与效率
- 在双臂机器人上实测有效，支持复杂多接触任务
设计涉及接触的操作轨迹极具挑战，需同时推理机器人、物体运动及复杂接触序列。本文提出一种新型分层优化框架，可高效同步规划机器人、物体及接触轨迹。首先利用混合整数线性规划（MILP）在近似动力学约束下选择最优接触对，再通过非线性规划（NLP）在完整非线性约束下优化机器人与物体轨迹。采用二进制编码技术对双线性约束进行凸松弛，使MILP获得更紧致的解并降低计算复杂度。框架在多种操作任务中验证，能有效处理复杂多接触交互，并实现显著计算优势。此外，在双臂机器人系统上完成硬件实验。视频演示见 https://youtu.be/s2S1Eg5RsRE?si=chPkftz_a3NAHxLq。
原文摘要 · Abstract (English)
Designing trajectories for manipulation through contact is challenging as it requires reasoning of object \& robot trajectories as well as complex contact sequences simultaneously. In this paper, we present a novel framework for simultaneously designing trajectories of robots, objects, and contacts efficiently for contact-rich manipulation. We propose a hierarchical optimization framework where Mixed-Integer Linear Program (MILP) selects optimal contacts between robot \& object using approximate dynamical constraints, and then a NonLinear Program (NLP) optimizes trajectory of the robot(s) and object considering full nonlinear constraints. We present a convex relaxation of bilinear constraints using binary encoding technique such that MILP can provide tighter solutions with better computational complexity. The proposed framework is evaluated on various manipulation tasks where it can reason about complex multi-contact interactions while providing computational advantages. We also demonstrate our framework in hardware experiments using a bimanual robot system. The video summarizing this paper and hardware experiments is found https://youtu.be/s2S1Eg5RsRE?si=chPkftz_a3NAHxLq
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。