Conditional Task and Motion Planning through an Effort-based Approach
This work addresses robot efficiency in dynamic environments, but it appears incremental as it builds on existing replanning approaches by adding effort minimization.
The paper tackles the problem of minimizing robot effort in conditional task and motion planning by proposing an algorithm that replans for effort savings, not just infeasibility, with effort defined as execution time and extensible to energy consumption. The result is a conditional and dynamically adaptable plan, with theoretical analysis suggesting completeness and scalability, though experiments are still in progress.
This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation.