Improving Generalization Ability of Robotic Imitation Learning by Resolving Causal Confusion in Observations
This work addresses generalization problems in robotic manipulation for imitation learning, but it is incremental as it builds on existing causal frameworks and architectures.
The paper tackled the poor generalization of robotic imitation learning under domain shifts by proposing a causal structure learning framework to resolve causal confusion in observations, demonstrating that it considerably mitigates generalization issues in simulation with ALOHA bimanual robot arms.
Recent developments in imitation learning have considerably advanced robotic manipulation. However, current techniques in imitation learning can suffer from poor generalization, limiting performance even under relatively minor domain shifts. In this work, we aim to enhance the generalization capabilities of complex imitation learning algorithms to handle unpredictable changes from the training environments to deployment environments. To avoid confusion caused by observations that are not relevant to the target task, we propose to explicitly learn the causal relationship between observation components and expert actions, employing a framework similar to [6], where a causal structural function is learned by intervention on the imitation learning policy. Disentangling the feature representation from image input as in [6] is hard to satisfy in complex imitation learning process in robotic manipulation, we theoretically clarify that this requirement is not necessary in causal relationship learning. Therefore, we propose a simple causal structure learning framework that can be easily embedded in recent imitation learning architectures, such as the Action Chunking Transformer [31]. We demonstrate our approach using a simulation of the ALOHA [31] bimanual robot arms in Mujoco, and show that the method can considerably mitigate the generalization problem of existing complex imitation learning algorithms.