A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream

• URL: http://arxiv.org/abs/2510.00182v1
• Date: Tue, 30 Sep 2025 19:03:14 GMT
• Title: A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream
• Authors: Jorge Mendez-Mendez,
• Abstract summary: Large language models (LLMs) to solve complex robotics problems requires understanding their planning capabilities.<n>One promising direction is to integrate the semantic knowledge of LLMs with the formal reasoning of task and motion planning.<n>We develop 16 algorithms that use Gemini 2.5 Flash to substitute key TAMP components.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using large language models (LLMs) to solve complex robotics problems requires understanding their planning capabilities. Yet while we know that LLMs can plan on some problems, the extent to which these planning capabilities cover the space of robotics tasks is unclear. One promising direction is to integrate the semantic knowledge of LLMs with the formal reasoning of task and motion planning (TAMP). However, the myriad of choices for how to integrate LLMs within TAMP complicates the design of such systems. We develop 16 algorithms that use Gemini 2.5 Flash to substitute key TAMP components. Our zero-shot experiments across 4,950 problems and three domains reveal that the Gemini-based planners exhibit lower success rates and higher planning times than their engineered counterparts. We show that providing geometric details increases the number of task-planning errors compared to pure PDDL descriptions, and that (faster) non-reasoning LLM variants outperform (slower) reasoning variants in most cases, since the TAMP system can direct the LLM to correct its mistakes.

Related papers

• Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models [9.925353344469324]
  Large Language Models (LLMs) can generate helpful actions and states to prune the search space.<n>We propose a novel planner integrated with problem decomposition, which first decomposes large planning problems into simpler sub-tasks.<n>We empirically validate the effectiveness of our planner across multiple domains, demonstrating the ability of search space partition when solving large-scale planning problems.
  arXiv  Detail & Related papers  (2025-08-15T15:08:07Z)
• Learning to Reason and Navigate: Parameter Efficient Action Planning with Large Language Models [63.765846080050906]
  This paper proposes a novel parameter-efficient action planner using large language models (PEAP-LLM) to generate a single-step instruction at each location.<n>Experiments show the superiority of our proposed model on REVERIE compared to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2025-05-12T12:38:20Z)
• LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language [17.914580097058106]
  Bimanual robotic manipulation presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands.<n>Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizon timescales.<n>This paper introduces LLM+MAP, a bimanual planning framework that integrates LLM reasoning and multi-agent planning.
  arXiv  Detail & Related papers  (2025-03-21T17:04:01Z)
• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [62.854649499866774]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.<n>We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents.<n>We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• CoMMIT: Coordinated Multimodal Instruction Tuning [90.1532838391285]
  Multimodal large language models (MLLMs) generally involve cooperative learning between a backbone LLM and a feature encoder of non-text input modalities.<n>In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.<n>We propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models [6.860460230412773]
  We propose an LLM-embodied path planning framework for mobile agents. Our proposed multi-layer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks.
  arXiv  Detail & Related papers  (2024-07-02T12:38:46Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• Improving Planning with Large Language Models: A Modular Agentic Architecture [6.394504490512812]
  Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning.<n>We propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of specialized modules.<n>We find that MAP yields significant improvements over both standard LLM methods.
  arXiv  Detail & Related papers  (2023-09-30T00:10:14Z)
• Plansformer: Generating Symbolic Plans using Transformers [24.375997526106246]
  Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP) We introduce Plansformer; an LLM fine-tuned on planning problems and capable of generating plans with favorable behavior in terms of correctness and length with reduced knowledge-engineering efforts. For one configuration of Plansformer, we achieve 97% valid plans, out of which 95% are optimal for Towers of Hanoi - a puzzle-solving domain.
  arXiv  Detail & Related papers  (2022-12-16T19:06:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.