让机器人主动转脖子，突破视野限制实现复杂操作
Imitation Learning for Active Neck Motion Enabling Robot Manipulation beyond the Field of View
- 通过动态颈部运动收集数据，降低遥控时的视角不适
- 在视点变化下仍达90%成功率，边缘物体操作表现更优
- 适合需要大范围视野的机器人任务，如非固定场景操作
以往深度模仿学习多采用固定摄像头输入，限制了任务仅能在预设视场内执行。本文提出一种新教学系统，通过主动控制机器人颈部运动，扩展模仿学习的应用范围，涵盖更复杂的动作如颈部姿态变化。该系统在遥操作中最小化因视角动态变化带来的不适感，同时构建包含颈部运动的数据集。进一步提出一种新型网络模型，可联合学习抓取与主动颈部运动。实验表明，该模型在视点变化干扰下仍保持约90%的成功率；尤其在物体位于视野边缘或超出标准视场的挑战性场景中，显著优于传统模型。所提方法提升了数据采集效率，拓展了模仿学习在复杂动态环境中的应用边界。
原文摘要 · Abstract (English)
Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However, enabling a robot to actively maneuver its neck can significantly expand the scope of imitation learning to encompass a wider variety of tasks and expressive actions such as neck gestures. To facilitate imitation learning in robots capable of neck movement while simultaneously performing object manipulation, we propose a teaching system that systematically collects datasets incorporating neck movements while minimizing discomfort caused by dynamic viewpoints during teleoperation. In addition, we present a novel network model for learning manipulation tasks including active neck motion. Experimental results showed that our model can achieve a high success rate of around 90\%, regardless of the distraction from the viewpoint variations by active neck motion. Moreover, the proposed model proved particularly effective in challenging scenarios, such as when objects were situated at the periphery or beyond the standard field of view, where traditional models struggled. The proposed approach contributes to the efficiency of dataset collection and extends the applicability of imitation learning to more complex and dynamic scenarios.
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