CSubBT: A Self-Adjusting Execution Framework for Mobile Manipulation System

TL;DR

CSubBT is a self-adjusting execution framework for mobile manipulation that enhances robustness by decomposing actions, detecting anomalies, and sampling new parameters during task execution, demonstrated through extensive experiments.

Contribution

The paper introduces CSubBT, a novel self-adjusting framework based on Behavior Trees that handles anomalies in mobile manipulation tasks by sampling new action parameters.

Findings

Demonstrates robustness in simulation and real-world experiments

Addresses anomalies as constraint satisfaction problems

Improves success rate of long-horizon manipulation tasks

Abstract

With the advancements in modern intelligent technologies, mobile robots equipped with manipulators are increasingly operating in unstructured environments. These robots can plan sequences of actions for long-horizon tasks based on perceived information. However, in practice, the planned actions often fail due to discrepancies between the perceptual information used for planning and the actual conditions. In this paper, we introduce the {\itshape Conditional Subtree} (CSubBT), a general self-adjusting execution framework for mobile manipulation tasks based on Behavior Trees (BTs). CSubBT decomposes symbolic action into sub-actions and uses BTs to control their execution, addressing any potential anomalies during the process. CSubBT treats common anomalies as constraint non-satisfaction problems and continuously guides the robot in performing tasks by sampling new action parameters in the constraint space when anomalies are detected. We demonstrate the robustness of our framework through extensive manipulation experiments on different platforms, both in simulation and real-world settings.