Conditional Task and Motion Planning through an Effort-based Approach

TL;DR

This paper introduces a novel conditional task and motion planning algorithm that minimizes robot effort, considering replanning for effort savings and adaptability to environmental changes, with promising theoretical analysis and ongoing experiments.

Contribution

It presents a new effort-based planning algorithm that dynamically adapts to environmental changes, extending beyond traditional replan-only-when-unfeasible methods.

Findings

The algorithm considers effort as execution time and can be extended to energy consumption.

Theoretical analysis suggests the algorithm is complete and scalable.

Experiments are underway to validate the approach.

Abstract

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.