LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
This addresses the domain-specific and labor-intensive limitations of conventional TAMP methods for robotics and AI applications, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the problem of Task and Motion Planning (TAMP) by introducing LLM^3, a framework that uses Large Language Models to propose symbolic actions and parameters, with motion failure reasoning for iterative refinement, achieving effectiveness in simulations and practical applicability on a physical manipulator.
Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, we present LLM^3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface. Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning. Crucially, LLM^3 incorporates motion planning feedback through prompting, allowing the LLM to iteratively refine its proposals by reasoning about motion failure. Consequently, LLM^3 interfaces between task planning and motion planning, alleviating the intricate design process of handling domain-specific messages between them. Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters. Ablation studies underscore the significant contribution of motion failure reasoning to the success of LLM^3. Furthermore, we conduct qualitative experiments on a physical manipulator, demonstrating the practical applicability of our approach in real-world settings.