发表机构
The University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MAVP通过预测并跟踪基座位姿目标，利用地图和定位反馈提升移动操作策略的执行可靠性，在六个真实任务中超越速度控制方法。
AI 中文摘要
成功的移动操作需要协调基座和手臂的运动，同时保持精确的空间定位。然而，基于示范训练的策略可能难以可靠地实现预期的基座运动，导致空间错位和随后的操作失败。我们提出了MAVP（地图感知视觉运动策略），这是一个通过预测明确的基座位姿目标并利用定位反馈进行跟踪来提高执行可靠性的框架。MAVP从遥操作示范中重建静态地图，并在共享的地图坐标系中表达示范的基座轨迹，从而在示范之间提供一致的空间监督。在执行时，策略接收RGB观测、关节状态以及机器人当前的地图坐标系基座位姿，并联合预测目标基座位姿、手臂动作和夹爪动作。一个低层控制器利用前馈运动和位姿误差反馈来跟踪预测的基座目标，从而能够纠正执行偏差。我们还在训练期间使用位姿噪声增强，以提高对策略位姿输入误差的鲁棒性。在六个真实世界操作任务和三个策略家族中，MAVP在所有任务中均取得了比无锚定速度控制更高的任务成功率。视频和其他结果可在以下网址获取：此https URL。
英文摘要
Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained policies can struggle to realise the intended base motion reliably, leading to spatial misalignment and subsequent manipulation failures. We present MAVP (Map-Aware Visuomotor Policies), a framework that improves execution reliability by predicting explicit base-pose targets and tracking them using localisation feedback. MAVP reconstructs a static map from teleoperated demonstrations and expresses demonstrated base trajectories in a shared map frame, providing consistent spatial supervision across demonstrations. At execution time, the policy receives RGB observations, joint states, and the robot's current map-frame base pose, and jointly predicts target base poses, arm actions, and gripper actions. A low-level controller tracks the predicted base targets using feedforward motion and pose error feedback, enabling correction of execution deviations. We additionally use pose-noise augmentation during training to improve robustness to errors in the policy's pose input. Across six real-world manipulation tasks and three policy families, MAVP achieves higher task success rates than unanchored velocity control in all tasks. Videos and additional results are available at https://123qwedsa123.github.io/mavp/.
