DynaMOMA：动态物体移动操作的抓取姿态瞬时预测
DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
AI总结：
提出DynaMOMA框架，结合基于锚点的扩散模型预测瞬时抓取轨迹与全身控制策略，实现动态物体移动操作，在仿真和真实实验中表现优异。
摘要：
移动操作是机器人学的基本任务，近年来发展迅速，使机器人能够在复杂环境中导航、到达并与物体交互。然而，动态物体的移动操作仍然极具挑战性，因为机器人必须协调移动基座和机械臂，同时适应不断变化的目标姿态。一个关键挑战在于从动态观测中预测时间一致的短时抓取轨迹。在这项工作中，我们提出DynaMOMA，一种动态移动操作框架，将瞬时抓取轨迹预测与全身控制策略相结合。我们的预测器使用基于锚点的扩散模型，根据历史观测生成时间一致的短时抓取轨迹。然后，预测轨迹被编码为紧凑特征，并输入到全身强化学习策略中，该策略控制移动操作器进行动态抓取。我们进一步引入一种预期引导奖励，通过将目标从当前抓取观测自适应地转移到瞬时预测的抓取轨迹，赋予策略预期抓取视野。通过在Isaac Gym仿真中的大量实验，我们展示了我们的方法在多种设置和抓取指标下，在动态物体移动操作中实现了强劲性能。此外，我们的预测器和策略在真实世界实验中表现出强大的泛化能力。
英文摘要：
Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objects in complex environments. However, mobile manipulation of dynamic objects remains highly challenging, as robots must coordinate the mobile base and arm while adapting to continuously evolving target poses. A key challenge lies in predicting temporally consistent short-horizon grasp trajectories from dynamic observations. In this work, we propose \ours{}, a dynamic mobile manipulation framework that couples instantaneous grasp trajectory prediction with whole-body control policy. Our predictor uses an anchor-based diffusion model to generate temporally consistent short-horizon grasp trajectories conditioned on historical observations. The predicted trajectories are then encoded as compact features and fed to a whole-body reinforcement learning policy, which controls the mobile manipulator for dynamic grasping. We further introduce a anticipation-guided reward that equips the policy with an anticipatory grasping horizon by adaptively shifting the target from the current grasp observation to the instantaneously predicted grasp trajectory. Through extensive experiments in Isaac Gym simulation, we show that our method achieves strong performance in mobile manipulation of dynamic objects across diverse settings and grasping metrics. Furthermore, our predictor and policy demonstrate strong generalizability in real-world experiments.
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。
