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.

翻译：复杂灵巧操作需要交替使用抓取与非抓取握持方式，并利用环境实现物体的滑动与旋转。本文提出一种能够推理多种接触变化以发现此类操作方案的操作规划器。该规划器采用混合方法：对旋转与滑动操作基元实施接触隐式轨迹优化，通过基于采样的规划切换操作基元及目标物体姿态。所提出的优化方法——轨迹优化与接触同步选择（STOCS）——引入无限规划框架，在操作基元执行过程中动态选择物体与环境间的接触点与支撑力。为编排操作基元序列，一种基于采样的树生长规划器利用STOCS构建操作树。研究表明，借助强大的轨迹优化器，所提规划器可在数十个样本内发现涉及抓取、滑动与旋转的多模态操作轨迹。经验证，这些轨迹能使6自由度机械臂成功操作实体物体。