In complex robotic systems, planning and plan execution techniques are pervasively deployed at different levels of abstraction combining task, path, and motion planning capabilities. AI Planning & Scheduling (P&S) methods are often crucial to enable intelligent robots to perform autonomous, flexible, and interactive behaviors. However, these methods and frameworks cannot be considered as stand-alone functionalities, but need to be deeply integrated into the overall robotic architectures in order to be effective. This requires collaboration between researchers from both the AI and the Robotics communities.
The aim of the workshop is to bring researchers from the AI P&S and Robotics communities together to exchange ideas, discuss techniques and problems as well as identify common open challenges related to robotic planning methods. The goal of the workshop is twofold. First, it will foster the discussion in the Robotics community about challenges related to AI planning for autonomous robots (deliberative, reactive, continuous planning and execution, etc.) as well as their expectations from the P&S community. Second, it will attract representatives from the AI P&S community to further develop their interests and efforts towards robotic problems and applications.
Relevant topics include (not limited to):
– Combined task, path, and motion planning;
– Integration of planning and plan execution methods in robotic architectures;
– Representation and learning of planning models for robotics;
– Optimization and scheduling techniques for robot planning and scheduling;
– Planning for long-term autonomy in robotics;
– Planning with uncertainty for reliable robots;
– Multi-robot planning, delegation, coordination, and execution;
– Human-aware planning and execution in (safe) human-robot interaction/collaboration;
– Mixed-initiative planning and adjustable autonomy;
– V&V of plan-based robot autonomy;
– Real-world planning applications for autonomous/interactive robots.