Student-Informed Teacher Training
This addresses a key challenge in imitation learning for robotics, enabling more effective learning from high-dimensional inputs like images, though it appears incremental as it builds on existing privileged imitation learning methods.
The paper tackles the problem of teacher-student asymmetry in privileged imitation learning, where students may fail to imitate teachers due to partial observability, by proposing a joint training framework that encourages teachers to learn imitable behaviors, resulting in improved performance on vision-based quadrotor flight and manipulation tasks.
Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher is trained with privileged task information, while a student tries to predict the actions of the teacher with more limited observations, e.g., in a robot navigation task, the teacher might have access to distances to nearby obstacles, while the student only receives visual observations of the scene. However, privileged imitation learning faces a key challenge: the student might be unable to imitate the teacher's behavior due to partial observability. This problem arises because the teacher is trained without considering if the student is capable of imitating the learned behavior. To address this teacher-student asymmetry, we propose a framework for joint training of the teacher and student policies, encouraging the teacher to learn behaviors that can be imitated by the student despite the latters' limited access to information and its partial observability. Based on the performance bound in imitation learning, we add (i) the approximated action difference between teacher and student as a penalty term to the reward function of the teacher, and (ii) a supervised teacher-student alignment step. We motivate our method with a maze navigation task and demonstrate its effectiveness on complex vision-based quadrotor flight and manipulation tasks.