We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any prior knowledge of the object being interacted with. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector's pose at the point where object interaction begins, as observed from the demonstration. By modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-supervised manner, by automatically moving the end-effector's camera around the object. At test time, the end-effector is moved to the estimated state through a linear path, at which point the demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory without actually needing to explicitly learn a policy. Real-world experiments on 8 everyday tasks show that our method can learn a diverse range of skills from just a single human demonstration, whilst also yielding a stable and interpretable controller.

本文介绍了一种用于视觉模仿学习的简单方法，可以从一个人类演示中学习机器人操作任务，而无需先前了解与对象的交互情况，并且可以通过自监督学习进行训练。在实现过程中，该方法将模仿学习建模为状态估计问题，以目标交互开始时末端执行器的姿态作为状态，在自监督训练过程中让末端执行器的摄像头围绕对象自动移动。 最终，在测试时，机器人通过一条线性路径移动到预测的状态，然后简单地重放演示的末端执行器速度，从而获得了一个复杂的交互轨迹，而无需明确学习策略。在常见的8个任务上进行的实现结果表明，这种方法可以从单个人类演示中学习出多样的技能，并产生稳定且可解释的控制器。

从单个演示到机器人操作的粗到细模仿学习