EHC-MM：面向移动操作的具体化整体控制
EHC-MM: Embodied Holistic Control for Mobile Manipulation
AI总结：
本文提出 EHC-MM 方法，将 DMCG 原则建模为 QP 问题并用 sig(w) 动态平衡移动与操作，结合 MPBS 实现底盘、臂和相机协调控制，显著提升移动操作成功率与效率。
摘要：
移动操作通常需要底盘负责移动、机械臂负责精确操作、相机负责感知。远距移动、近距抓取（DMCG）原则对于整体控制至关重要。我们提出面向移动操作的具体化整体控制（EHC-MM），并引入具体化函数 sig(w)：通过将 DMCG 原则形式化为二次规划（QP）问题，sig(w) 在考虑机器人状态和环境的情况下，动态平衡机器人在移动与操作之间的侧重。此外，我们提出基于监控位置伺服（MPBS）并结合 sig(w)，使机器人在操作过程中能够持续跟踪目标。该方法实现了机器人底盘、机械臂和相机之间的协调控制，从而提升任务效率。通过大量仿真和真实世界实验，我们的方法显著提高了移动操作任务的成功率和效率，在真实场景中实现了 95.6% 的成功率，并将时间效率提升了 52.8%。
英文摘要：
Mobile manipulation typically entails the base for mobility, the arm for accurate manipulation, and the camera for perception. The principle of Distant Mobility, Close Grasping(DMCG) is essential for holistic control. We propose Embodied Holistic Control for Mobile Manipulation(EHC-MM) with the embodied function of sig(w): By formulating the DMCG principle as a Quadratic Programming (QP) problem, sig(w) dynamically balances the robot's emphasis between movement and manipulation with the consideration of the robot's state and environment. In addition, we propose the Monitor-Position-Based Servoing (MPBS) with sig(w), enabling the tracking of the target during the operation. This approach enables coordinated control among the robot's base, arm, and camera, enhancing task efficiency. Through extensive simulations and real-world experiments, our approach significantly improves both the success rate and efficiency of mobile manipulation tasks, achieving a 95.6% success rate in real-world scenarios and a 52.8% increase in time efficiency.
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。
