Organizers: Neil Dantam, Swarat Chaudhuri, Lydia Kavraki
Complex robot behavior requires not only paths to navigate or reach objects, but also decisions about which objects to reach, in what order, and what style of action to perform. Such decisions combine the need for continuous, collision-free motion planning with the discrete actions of task planning. Efficient algorithms exist to solve these parts in isolation; however, integrating task and motion planning presents algorithmic challenges in generality, scalability, completeness. Task-Motion Planning (TMP) is an integrated approach to this challenge which has developed in the traditional robotics community. With this workshop, we hope to strengthen connections to the AI and formal methods communities.
Challenges in TMP arise from the interaction of task and motion layers. Task actions affect motion planning feasibility, and motion plan feasibility dictates the ability to perform task actions. Current work on TMP has achieved good performance by focusing on specific types of actions or solving expected-case scenarios, and ongoing advances are improving completeness, generality, and optimality.
This workshop will highlight recent applications and explore new methods for combining task and motion planning. We include speakers from beyond the typical robotics community, to identify connections with work in AI and cyber-physical systems. Furthermore, we will present and discuss a benchmark set in development since the 2016 Workshop on Task and Motion Planning. From this workshop, we expect participating researchers to identify and address important challenges, techniques, and benchmarks necessary for combining task and motion planning.