Task and motion planning is one of the key problems in robotics today. It is often formulated as a discrete task allocation problem combined with continuous motion planning. Many existing approaches to TAMP involve explicit descriptions of task primitives that cause discrete changes in the kinematic relationship between the actor and the objects. In this work we propose an alternative approach to TAMP which does not involve explicit enumeration of task primitives. Instead, the actions are represented implicitly as part of the solution to a nonlinear optimization problem. We focus on decision making for robotic manipulators, specifically for pick and place tasks, and show several possible extensions. We explore the efficacy of the model through a number of simulated experiments involving multiple robots, objects and interactions with the environment.

翻译：任务与运动规划是当今机器人领域的关键问题之一，通常被建模为离散任务分配问题与连续运动规划的联合优化。现有许多TAMP方法需要显式描述引发执行者与物体之间运动学关系离散变化的任务原语。本研究提出一种替代性TAMP方案，无需显式枚举任务原语，而是将动作隐含地表示为非线性优化问题解的一部分。我们聚焦于机器人操纵器的决策制定，特别是抓取与放置任务，并展示了若干可能的扩展方向。通过多项涉及多机器人、多物体及环境交互的仿真实验，验证了该模型的有效性。