Learning from demonstrations (LfD) methods guide learning agents to a desired solution using demonstrations from a teacher. While some LfD methods can handle small mismatches in the action spaces of the teacher and student, here we address the case where the teacher demonstrates the task in an action space that can be substantially different from that of the student -- thereby inducing a large action space mismatch. We bridge this gap with a framework, Morphological Adaptation in Imitation Learning (MAIL), that allows training an agent from demonstrations by other agents with significantly different morphologies (from the student or each other). MAIL is able to learn from suboptimal demonstrations, so long as they provide some guidance towards a desired solution. We demonstrate MAIL on challenging household cloth manipulation tasks and introduce a new DRY CLOTH task -- cloth manipulation in 3D task with obstacles. In these tasks, we train a visual control policy for a robot with one end-effector using demonstrations from a simulated agent with two end-effectors. MAIL shows up to 27% improvement over LfD and non-LfD baselines. It is deployed to a real Franka Panda robot, and can handle multiple variations in cloth properties (color, thickness, size, material) and pose (rotation and translation). We further show generalizability to transfers from n-to-m end-effectors, in the context of a simple rearrangement task.

使用Morphological Adaptation in Imitation Learning (MAIL)框架，从3D带障碍物情况下，带有两个末端执行器的机器人的演示中训练出一个末端执行器的Franka Panda机器人的可视化控制策略，比Learning from Demonstrations和非Learning from Demonstrations基线方法提高了27%的成功率，并且在面对不同颜色、厚度、大小和材料等多变性的衣物的姿态（旋转和平移）时展现出很好的通用性。

弥合示范学习中的动作空间不匹配