AVR：基于视点与焦距优化的主动视觉驱动精确机器人操作
AVR: Active Vision-Driven Precise Robot Manipulation with Viewpoint and Focal Length Optimization
AI总结：
AVR通过主动视觉框架，结合视点控制与焦距优化，提升复杂场景中机器人操作的精确感知与成功率，在仿真和真实实验中均显著优于静态视点基线。
摘要：
复杂场景中的机器人操作需要对任务相关细节进行精确感知，然而固定或次优的视点往往会损害细粒度感知并引发遮挡，从而限制模仿学习策略的性能。我们提出了AVR（主动视觉驱动机器人学），一个双臂遥操作与学习框架，该框架将头部追踪视点控制（头戴显示器到2自由度云台）与电动光学变焦统一起来，以在数据采集和部署期间将目标保持在适当尺度的中心位置。在仿真中，一个AVR插件通过模拟主动视觉（基于感兴趣区域的视点变化、保持宽高比的裁剪并带有显式变焦比例，以及超分辨率）来增强RoboTwin演示，在各种操作任务中取得了5%至17%的成功率提升。在我们的真实世界平台上，AVR在大多数任务上提升了成功率，与静态视点基线相比提升超过25%，进一步的研究还展示了在遮挡、杂乱和光照干扰下的鲁棒性，以及对未见环境和物体的泛化能力。这些结果为未来追求人类级灵巧性和精度的机器人精确操作方法铺平了道路。
英文摘要：
Robotic manipulation in complex scenes demands precise perception of task-relevant details, yet fixed or suboptimal viewpoints often impair fine-grained perception and induce occlusions, constraining imitation-learned policies. We present AVR (Active Vision-driven Robotics), a bimanual teleoperation and learning framework that unifies head-tracked viewpoint control (HMD-to-2-DoF gimbal) with motorized optical zoom to keep targets centered at an appropriate scale during data collection and deployment. In simulation, an AVR plugin augments RoboTwin demonstrations by emulating active vision (ROI-conditioned viewpoint change, aspect-ratio-preserving crops with explicit zoom ratios, and super-resolution), yielding 5-17% gains in task success across diverse manipulations. On our real-world platform, AVR improves success on most tasks, with over 25% gains compared to the static-view baseline, and extended studies further demonstrate robustness under occlusion, clutter, and lighting disturbances, as well as generalization to unseen environments and objects. These results pave the way for future robotic precision manipulation methods in the pursuit of human-level dexterity and precision.
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