CSubBT：一种用于移动操作系统的自适应执行框架
CSubBT: A Self-Adjusting Execution Framework for Mobile Manipulation System
AI总结：
针对移动机器人因感知与实际差异导致规划动作失败的问题，提出基于行为树的自适应执行框架CSubBT，通过分解动作、约束采样处理异常，提升操作鲁棒性。
摘要：
随着现代智能技术的进步，配备机械臂的移动机器人在非结构化环境中的操作日益增多。这些机器人能基于感知信息为长时程任务规划动作序列。然而实际中，由于规划所用感知信息与实际情况存在差异，规划动作常失败。本文提出基于行为树（BTs）的移动操作任务通用自适应执行框架——条件子树（CSubBT）。CSubBT将符号动作分解为子动作并用BTs控制其执行，以处理过程中的潜在异常。它将常见异常视为约束不满足问题，并在检测到异常时通过在约束空间中采样新动作参数，持续引导机器人执行任务。我们通过在不同平台上的大量操作实验，证明了该框架在仿真和真实环境中的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
