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CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Yishuai Cai, Xinglin Chen, Yunxin Mao, Kun Hu, Minglong Li, Yaodong Yang, Yuanpei Chen

Abstract

Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded -- comprising high-level action models and low-level control policies -- which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and consistent BT system. We analyze its complexity and introduce CABTO (Context-Aware Behavior Tree grOunding), the first framework to efficiently solve this challenge. CABTO leverages pre-trained Large Models (LMs) to heuristically search the space of action models and control policies, guided by contextual feedback from BT planners and environmental observations. Experiments spanning seven task sets across three distinct robotic manipulation scenarios demonstrate CABTO's effectiveness and efficiency in generating complete and consistent behavior tree systems.

CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Abstract

Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded -- comprising high-level action models and low-level control policies -- which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and consistent BT system. We analyze its complexity and introduce CABTO (Context-Aware Behavior Tree grOunding), the first framework to efficiently solve this challenge. CABTO leverages pre-trained Large Models (LMs) to heuristically search the space of action models and control policies, guided by contextual feedback from BT planners and environmental observations. Experiments spanning seven task sets across three distinct robotic manipulation scenarios demonstrate CABTO's effectiveness and efficiency in generating complete and consistent behavior tree systems.
Paper Structure (29 sections, 5 equations, 4 figures, 3 tables, 2 algorithms)

This paper contains 29 sections, 5 equations, 4 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Behavior Tree Generation
  4. High-Level Action Models
  5. Low-Level Control Policies
  6. Preliminaries
  7. Behavior Tree
  8. BT System
  9. BT Planning
  10. Problem Formulation
  11. Methodology
  12. Naive Algorithm for BT Grounding
  13. High-level Model Proposal
  14. Low-Level Policy Sampling
  15. Cross-Level Refinement
  16. ...and 14 more sections

Figures (4)

  • Figure 1: Concepts involved in the BT grounding problem. (a) A BT is a directed rooted tree with behavior nodes and control nodes. (b) The solution is a complete and consistent BT system for the given task set. (c) A complete BT system can generate solution BTs for all tasks during the high-level BT planning based on action models. (d) A consistent BT system ensures its control policies result in state transitions consistent with their action models during low-level BT execution.
  • Figure 2: The framework of CABTO includes three phases: (1) High-level model proposal leverages the planning contexts for the LLMs to heuristically explore the space of action models; (2) Low-level policy sampling leverages the execution contexts for the VLMs to heuristically explore the space of control policies; (3) Cross-level refinement leverages both planning and execution contexts for refining inconsistent action models.
  • Figure 3: Configurations of the single-arm and dual-arm Franka manipulation tasks in Isaac Sim.
  • Figure 4: The deployment of CABTO in OmniGibson: Given a task set, CABTO generates a complete and consistent BT system. For a specific task, BT planning is used to generate the solution BT. Then the BT is executed, enabling the robot to successfully achieve the goal.

Theorems & Definitions (2)

  • Definition 3.1: Completeness
  • Definition 3.2: Consistency