Efficient Behavior Tree Planning with Commonsense Pruning and Heuristic

• URL: http://arxiv.org/abs/2406.00965v2
• Date: Tue, 4 Jun 2024 01:41:24 GMT
• Title: Efficient Behavior Tree Planning with Commonsense Pruning and Heuristic
• Authors: Xinglin Chen, Yishuai Cai, Yunxin Mao, Minglong Li, Zhou Yang, Wen Shanghua, Wenjing Yang, Weixia Xu, Ji Wang,
• Abstract summary: Behavior Tree (BT) planning is crucial for autonomous robot behavior control, yet its application in complex scenarios is hampered by long planning times. This paper proposes improving BT planning for everyday service robots leveraging commonsense reasoning provided by Large Language Models (LLMs) We introduce a learnable and transferable commonsense library to enhance the LLM's reasoning performance without fine-tuning.
• Score: 5.560092034823088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Behavior Tree (BT) planning is crucial for autonomous robot behavior control, yet its application in complex scenarios is hampered by long planning times. Pruning and heuristics are common techniques to accelerate planning, but it is difficult to design general pruning strategies and heuristic functions for BT planning problems. This paper proposes improving BT planning efficiency for everyday service robots leveraging commonsense reasoning provided by Large Language Models (LLMs), leading to model-free pre-planning action space pruning and heuristic generation. This approach takes advantage of the modularity and interpretability of BT nodes, represented by predicate logic, to enable LLMs to predict the task-relevant action predicates and objects, and even the optimal path, without an explicit action model. We propose the Heuristic Optimal Behavior Tree Expansion Algorithm (HOBTEA) with two heuristic variants and provide a formal comparison and discussion of their efficiency and optimality. We introduce a learnable and transferable commonsense library to enhance the LLM's reasoning performance without fine-tuning. The action space expansion based on the commonsense library can further increase the success rate of planning. Experiments show the theoretical bounds of commonsense pruning and heuristic, and demonstrate the actual performance of LLM learning and reasoning with the commonsense library. Results in four datasets showcase the practical effectiveness of our approach in everyday service robot applications.

Related papers

• HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking [109.09735490692202]
  We propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.<n> Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.
  arXiv  Detail & Related papers  (2025-05-05T02:38:58Z)
• World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning [60.100794160682646]
  We propose a new learning framework that jointly optimize state prediction and action selection through preference learning. To automatically collect trajectories and stepwise preference data without human annotation, we introduce a tree search mechanism for extensive exploration via trial-and-error. Our method significantly outperforms existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and LLaMA-3.2 (11B)
  arXiv  Detail & Related papers  (2025-03-13T15:49:56Z)
• MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration [6.239895985962529]
  Multi-robot task planning and collaboration are critical challenges in robotics. We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness. We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot.
  arXiv  Detail & Related papers  (2025-02-25T10:39:28Z)
• 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.