Task and motion planning via language model inferred constraints

Abstract

Classical Task and Motion Planning (TAMP) systems are capable of solving complex and long-horizon tasks by leveraging models of a robot and its environment to explicitly reason about both discrete and continuous values in the robotics problem. While such systems are powerful on the set of problems they have been designed for, they do not transfer to novel problems for which their models are unspecified. The present disclosure integrates a language model together with a TAMP system for solving novel robotics problems, including using the language model to infer constraints for a specified robotic goal which can then be used by the TAMP system for generating a motion plan to achieve the robotic goal.

Dependencies are inferred from the claim text.