HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

• URL: http://arxiv.org/abs/2406.00965v4
• Date: Thu, 10 Oct 2024 02:36:53 GMT
• Title: HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning
• Authors: Yishuai Cai, Xinglin Chen, Yunxin Mao, Minglong Li, Shaowu Yang, Wenjing Yang, Ji Wang,
• Abstract summary: Heuristic Behavior Tree Planning (HBTP) is a reliable and efficient framework for BT generation. This paper introduces the BT expansion process, along with two variants designed for optimal planning and satising planning. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.
• Score: 6.2560501421348
• License:
• Abstract: Behavior Trees (BTs) are increasingly becoming a popular control structure in robotics due to their modularity, reactivity, and robustness. In terms of BT generation methods, BT planning shows promise for generating reliable BTs. However, the scalability of BT planning is often constrained by prolonged planning times in complex scenarios, largely due to a lack of domain knowledge. In contrast, pre-trained Large Language Models (LLMs) have demonstrated task reasoning capabilities across various domains, though the correctness and safety of their planning remain uncertain. This paper proposes integrating BT planning with LLM reasoning, introducing Heuristic Behavior Tree Planning (HBTP)-a reliable and efficient framework for BT generation. The key idea in HBTP is to leverage LLMs for task-specific reasoning to generate a heuristic path, which BT planning can then follow to expand efficiently. We first introduce the heuristic BT expansion process, along with two heuristic variants designed for optimal planning and satisficing planning, respectively. Then, we propose methods to address the inaccuracies of LLM reasoning, including action space pruning and reflective feedback, to further enhance both reasoning accuracy and planning efficiency. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.

Related papers

• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions [5.31484618181979]
  Behavior Tree (BT) is an appropriate control architecture for robots executing tasks following human instructions. This paper proposes a two-stage framework for BT generation, which first employs large language models to interpret goals from high-level instructions. We represent goals as well-formed formulas in first-order logic, effectively bridging intent understanding and optimal behavior planning.
  arXiv  Detail & Related papers  (2024-05-13T05:23:48Z)
• Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach [57.788675205519986]
  We learn high-quality traces from POMDP executions generated by any solver. We exploit data- and time-efficient Indu Logic Programming (ILP) to generate interpretable belief-based policy specifications. We show that learneds expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specifics within lower computational time.
  arXiv  Detail & Related papers  (2024-02-29T15:36:01Z)
• Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
  We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
  arXiv  Detail & Related papers  (2024-02-01T15:18:33Z)
• A Study on Training and Developing Large Language Models for Behavior Tree Generation [22.632022793663516]
  This paper presents an innovative exploration of the application potential of large language models (LLM) The core contribution of this paper lies in the design of a BT generation framework based on LLM. In order to ensure the effectiveness and executability of the generated BTs, we emphasize the importance of data verification.
  arXiv  Detail & Related papers  (2024-01-16T03:28:29Z)
• Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability [6.4111574364474215]
  The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios.
  arXiv  Detail & Related papers  (2024-01-15T18:01:59Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought [95.37585041654535]
  Embodied AI is capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI. Experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering.
  arXiv  Detail & Related papers  (2023-05-24T11:04:30Z)
• A Framework for Neurosymbolic Robot Action Planning using Large Language Models [3.0501524254444767]
  We present a framework aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL) Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.
  arXiv  Detail & Related papers  (2023-03-01T11:54:22Z)
• Visual Learning-based Planning for Continuous High-Dimensional POMDPs [81.16442127503517]
  Visual Tree Search (VTS) is a learning and planning procedure that combines generative models learned offline with online model-based POMDP planning. VTS bridges offline model training and online planning by utilizing a set of deep generative observation models to predict and evaluate the likelihood of image observations in a Monte Carlo tree search planner. We show that VTS is robust to different observation noises and, since it utilizes online, model-based planning, can adapt to different reward structures without the need to re-train.
  arXiv  Detail & Related papers  (2021-12-17T11:53:31Z)

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.