一次性双臂模仿学习
One-Shot Dual-Arm Imitation Learning
AI总结：
本文提出一次性双臂模仿学习 ODIL，通过三阶段视觉伺服实现精确对齐，仅需一次演示即可让双臂机器人学会协调任务，无需额外数据或先验知识，并在真实机器人上验证了其领先性能和鲁棒性。
摘要：
我们提出一次性双臂模仿学习（ODIL），使双臂机器人能够仅通过一次任务演示就学会精确且协调的日常任务。ODIL 采用一种新的三阶段视觉伺服（3-VS）方法，在末端执行器与目标物体之间实现精确对齐，之后重放演示轨迹即可完成任务。整个过程无需预先的任务或物体知识，也无需在单次演示后进行额外的数据收集和训练。此外，我们提出了一种新的双臂协调范式，用于从单次演示中学习双臂任务。ODIL 在真实双臂机器人上进行了测试，在 4 自由度（4-DoF）和 6 自由度（6-DoF）设置下的六项精确协调任务中均展现出最先进的性能，并在存在干扰物体和部分遮挡的情况下表现出鲁棒性。视频可在以下网址获取：https://www.robot-learning.uk/one-shot-dual-arm。
英文摘要：
We introduce One-Shot Dual-Arm Imitation Learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task. ODIL uses a new three-stage visual servoing (3-VS) method for precise alignment between the end-effector and target object, after which replay of the demonstration trajectory is sufficient to perform the task. This is achieved without requiring prior task or object knowledge, or additional data collection and training following the single demonstration. Furthermore, we propose a new dual-arm coordination paradigm for learning dual-arm tasks from a single demonstration. ODIL was tested on a real-world dual-arm robot, demonstrating state-of-the-art performance across six precise and coordinated tasks in both 4-DoF and 6-DoF settings, and showing robustness in the presence of distractor objects and partial occlusions. Videos are available at: https://www.robot-learning.uk/one-shot-dual-arm.
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。
