Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

classification cs.RO
keywords llmstaskplanningroboticabilityassemblybeenbehavior
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

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

    cs.RO 2025-08 conditional novelty 6.0 of 10

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

  2. Robot Operation of Home Appliances by Reading User Manuals

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A robot system that constructs a symbolic appliance model from a user manual and uses it to reliably execute natural language appliance operation tasks, outperforming direct VLM-based policies.

Pith tools