LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
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.
Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 39 of its 54 references that are in the map. First posted on arXiv 18 Mar 2024.
Builds on
39 of 54 references.
Closest ancestors · 2
Same direction, newest first
- 2023Task and Motion Planning with Large Language Models for Object Rearrangement10 Mar
- 2022Inner Monologue: Embodied Reasoning through Planning with Language Models12 Jul
Other references · 37
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- 2020Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Imagecited by 45
- 2019VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learningcited by 29
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- 2020Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Taskscited by 15
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- 2021Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspectivecited by 11
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- 2023LEMMA: Learning Language-Conditioned Multi-Robot Manipulationcited by 7
- 2023Sequential Manipulation Planning for Over-actuated Unmanned Aerial Manipulatorscited by 2
- 2023Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planningcited by 2
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