MoTo：面向通用移动操作的零样本即插即用交互感知导航
MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
AI总结：
针对传统移动操作泛化性差的问题，提出即插即用模块MoTo，结合交互关键点框架与轨迹优化，实现零样本移动操作，在OVMM基准和真实场景中成功率均优于现有方法。
摘要：
移动操作是机器人领域的核心挑战，它使机器人能够在多样化的任务和动态日常环境中为人类提供协助。传统移动操作方法由于缺乏大规模训练，往往难以在不同任务和环境间泛化。然而，近期操作基础模型的进展虽在各类固定基座操作任务上展现出出色的泛化能力，但仍局限于固定场景。为此，我们设计了名为MoTo的即插即用模块，可与任意现成的操作基础模型结合，赋予其移动操作能力。具体而言，我们提出一种交互感知导航策略，为通用移动操作生成机器人停靠点。为实现零样本能力，我们通过视觉语言模型（VLM）在多视图一致性约束下构建交互关键点框架，同时覆盖指令对应的目标物体和机械臂，该框架可直接应用固定基座操作基础模型。我们进一步提出面向移动基座和机械臂的运动规划目标，最小化两类关键点之间的距离并保证轨迹的物理可行性。通过这种方式，MoTo引导机器人移动至可成功执行固定基座操作的停靠点，借助VLM生成和轨迹优化以零样本方式实现移动操作，无需任何移动操作专家数据。在OVMM基准和真实场景上的大量实验结果表明，在无需额外训练数据的情况下，MoTo的成功率分别比当前最优的移动操作方法高出2.68%和16.67%。
英文摘要：
Mobile manipulation stands as a core challenge in robotics, enabling robots to assist humans across varied tasks and dynamic daily environments. Conventional mobile manipulation approaches often struggle to generalize across different tasks and environments due to the lack of large-scale training. However, recent advances in manipulation foundation models demonstrate impressive generalization capability on a wide range of fixed-base manipulation tasks, which are still limited to a fixed setting. Therefore, we devise a plug-in module named MoTo, which can be combined with any off-the-shelf manipulation foundation model to empower them with mobile manipulation ability. Specifically, we propose an interaction-aware navigation policy to generate robot docking points for generalized mobile manipulation. To enable zero-shot ability, we propose an interaction keypoints framework via vision-language models (VLM) under multi-view consistency for both target object and robotic arm following instructions, where fixed-base manipulation foundation models can be employed. We further propose motion planning objectives for the mobile base and robot arm, which minimize the distance between the two keypoints and maintain the physical feasibility of trajectories. In this way, MoTo guides the robot to move to the docking points where fixed-base manipulation can be successfully performed, and leverages VLM generation and trajectory optimization to achieve mobile manipulation in a zero-shot manner, without any requirement on mobile manipulation expert data. Extensive experimental results on OVMM and real-world demonstrate that MoTo achieves success rates of 2.68% and 16.67% higher than the state-of-the-art mobile manipulation methods, respectively, without requiring additional training data.
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。
