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LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning

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arxiv 2409.10444 v3 pith:Q2YFEJLU submitted 2024-09-16 cs.RO

LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning

classification cs.RO
keywords llmstaskplanningroboticabilityassemblybeenbehavior
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for their modularity and flexibility, but creating them manually can be effort-intensive. Large language models (LLMs) have recently been applied to robotic task planning for generating action sequences, yet their ability to generate BTs has not been fully investigated. To this end, we propose LLM-as-BT-Planner, a novel framework that leverages LLMs for BT generation in robotic assembly task planning. Four in-context learning methods are introduced to utilize the natural language processing and inference capabilities of LLMs for producing task plans in BT format, reducing manual effort while ensuring robustness and comprehensibility. Additionally, we evaluate the performance of fine-tuned smaller LLMs on the same tasks. Experiments in both simulated and real-world settings demonstrate that our framework enhances LLMs' ability to generate BTs, improving success rate through in-context learning and supervised fine-tuning.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Sequential Planning via Anchored Robotic Keypoints

    cs.RO 2026-06 unverdicted novelty 6.0

    SPARK reaches 43.7% success on six LIBERO-PRO cells by LLM-generated typed behavior trees plus multi-prompt perception and recovery, more than doubling CaP-Agent0 and VLA baselines.

  2. LLM-Driven Self-Refinement for Embodied Drone Task Planning

    cs.RO 2025-08 conditional novelty 6.0

    SRDrone combines continuous state evaluation with hierarchical Behavior Tree repair so that LLM-based drone planners can autonomously refine their own plans after failures.

  3. From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles

    cs.CV 2026-01 unverdicted novelty 4.0

    An agentic LLM/LVM framework generates adaptive behavior trees on-the-fly for AV navigation in CARLA+Nav2 simulation, succeeding in obstacle avoidance where static BTs fail.

  4. Large Language Model Agent: A Survey on Methodology, Applications and Challenges

    cs.CL 2025-03 accept novelty 3.0

    A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.