Sampling-based methods for factored task and motion planning

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constr...

Sample-Based Methods for Factored Task and Motion Planning

There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds in which...

Sampling-Based Motion Planning

Sampling-based algorithms for optimal motion planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a functio...

Hybrid Planning: Task-Space Control and Sampling-Based Planning

We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue, that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Hence, it is augmented with a globally acting planne...