面向多模态操作规划的轨迹优化与接触选择同步方法
Simultaneous Trajectory Optimization and Contact Selection for Multi-Modal Manipulation Planning
AI总结：
针对复杂灵巧操作的多接触变化需求，提出结合STOCS接触隐式优化与采样式树生长规划的混合操作规划器，仅需几十次采样即可生成含抓取、滑动、枢转的多模态轨迹，并经6自由度机械臂验证有效。
摘要：
复杂的灵巧操作需要在抓持式与非抓持式抓取之间切换，并使物体相对于环境滑动和枢转。本文提出一种操作规划器，能够推理多种接触变化以发现此类规划。该规划器采用混合方法：针对枢转和滑动操作基元执行接触隐式轨迹优化，同时通过基于采样的规划在不同操作基元与目标物体位姿之间切换。所提出的同步轨迹优化与接触选择方法（STOCS）引入无限规划框架，在单个操作基元执行过程中动态选择物体与环境间的接触点和支撑力。为实现操作基元的序列编排，基于采样的树生长规划器利用STOCS构建操作树。实验表明，借助强大的轨迹优化器，该规划器仅需几十次采样即可发现包含抓取、滑动和枢转的多模态操作轨迹。所得轨迹经验证可使6自由度机械臂成功操控实体物体。
英文摘要：
Complex dexterous manipulations require switching between prehensile and non-prehensile grasps, and sliding and pivoting the object against the environment. This paper presents a manipulation planner that is able to reason about diverse changes of contacts to discover such plans. It implements a hybrid approach that performs contact-implicit trajectory optimization for pivoting and sliding manipulation primitives and sampling-based planning to change between manipulation primitives and target object poses. The optimization method, simultaneous trajectory optimization and contact selection (STOCS), introduces an infinite programming framework to dynamically select from contact points and support forces between the object and environment during a manipulation primitive. To sequence manipulation primitives, a sampling-based tree-growing planner uses STOCS to construct a manipulation tree. We show that by using a powerful trajectory optimizer, the proposed planner can discover multi-modal manipulation trajectories involving grasping, sliding, and pivoting within a few dozen samples. The resulting trajectories are verified to enable a 6 DoF manipulator to manipulate physical objects successfully.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
