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We propose Text2Motion, a language-based planning framework enabling robotsto solve sequential manipulation tasks that require long-horizon reasoning.Given a natural language instruction, our framework constructs both a task- andmotion-level plan that is verified to reach inferred symbolic goals.Text2Motion uses feasibility heuristics encoded in Q-functions of a library ofskills to guide task planning with Large Language Models. Whereas previouslanguage-based planners only consider the feasibility of individual skills,Text2Motion actively resolves geometric dependencies spanning skill sequencesby performing geometric feasibility planning during its search. We evaluate ourmethod on a suite of problems that require long-horizon reasoning,interpretation of abstract goals, and handling of partial affordanceperception. Our experiments show that Text2Motion can solve these challengingproblems with a success rate of 82%, while prior state-of-the-artlanguage-based planning methods only achieve 13%. Text2Motion thus providespromising generalization characteristics to semantically diverse sequentialmanipulation tasks with geometric dependencies between skills.

Text2Motion: From Natural Language Instructions to Feasible Plans