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Automatic Behavior Tree Expansion with LLMs for Robotic Manipulation

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arxiv 2409.13356 v1 pith:QM2W5DHM submitted 2024-09-20 cs.RO

Jonathan Styrud , Matteo Iovino , Mikael Norrlöf , M{aa}rten Björkman , Christian Smith This is my paper · ORCID
classification cs.RO
keywords behaviormethodtasksconfigureexpansionmanipulationpolicyrobotic
verification ladder T0 review T1 audit T2 compute T3 formal
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Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks or unpredictable environments, while keeping a transparent policy that is readable and verifiable by humans. We propose the method BEhavior TRee eXPansion with Large Language Models (BETR-XP-LLM) to dynamically and automatically expand and configure Behavior Trees as policies for robot control. The method utilizes an LLM to resolve errors outside the task planner's capabilities, both during planning and execution. We show that the method is able to solve a variety of tasks and failures and permanently update the policy to handle similar problems in the future.

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Cited by 3 Pith papers

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

  1. Visual-Language-Guided Task Planning for Horticultural Robots

    cs.RO 2026-01 conditional novelty 6.0 of 10

    A vision-language model drives a simulated greenhouse robot through simple crop-inspection tasks with ~87% success, but long multi-target tasks collapse to under 10% success.

  2. VLM-driven Behavior Tree for Context-aware Task Planning

    cs.RO 2025-01 conditional novelty 5.0 of 10

    A VLM-generated behavior tree with self-prompted visual conditions lets a real robot branch on what it sees, clearing cups correctly in 8/10 cafe trials.

  3. Automatic Robot Task Planning by Integrating Large Language Model with Genetic Programming

    cs.RO 2025-02 conditional novelty 3.0 of 10

    An LLM generates robot behavior trees that are filtered by fitness and then evolved by genetic programming, reaching good task plans in fewer generations than starting from random trees.

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