Domain Adaptive Imitation Learning with Visual Observation

Introduction to Domain Adaptive Imitation Learning

Imitation learning (IL) is a strategy where an AI agent is trained to perform tasks by mimicking an expert. Unlike traditional reinforcement learning, in IL the agent learns from demonstrations without explicit reward signals, which can alleviate the need for hand-crafted reward functions. However, conventional IL assumes both the expert and learner share the same environment. In practice, this is often not the case—a scenario termed as "domain shift." Overcoming this hurdle is crucial when, for example, training a self-driving car with simulation data to be used in real-world scenarios.

Addressing Domain Shift with Visual Observations

One can encounter domain shift across various dimensions such as viewpoint variations, changes in visual effects, or differences in robot embodiment. When learning from visual observations, the challenge is exacerbated because images contain high-dimensional data, and minor changes between domains can significantly affect the learned policy, leading to unstable learning.

Proposed Method: D3IL

This work introduces a new method for domain-adaptive IL with visual observations, aiming to significantly improve performance on tasks with domain shift. The approach, named D3IL (Dual feature extraction and Dual cycle-consistency for Domain adaptive IL with visual observation), uses dual feature extraction and image reconstruction techniques. D3IL identifies behavioral features in observed actions that are independent of the domain, which can be used to train the learner effectively. Empirical results demonstrate that D3IL outperforms existing algorithms in situations involving substantial domain shift.

Deep Dive into D3IL

D3IL's architecture incorporates dual feature extraction, generating both domain and behavior feature vectors while ensuring these features are independent and retain the necessary information. D3IL also introduces a cycle-consistency check that entails a two-step process: first, images are reconstructed from extracted features, and then these images are used to re-extract features, ideally matching the originals. To improve feature extraction beyond the conventional adversarial learning block, D3IL uses this dual cycle-consistency alongside image and feature reconstruction consistency to refine the feature extraction process.

Performance and Experiments

The performance of D3IL is evaluated against existing methods across multiple tasks featuring varying domain shifts, such as changes in visual effects and robot morphology. The results show large margins of improvement in performance using D3IL, even in challenging scenarios when direct RL is difficult. These findings suggest that D3IL could potentially facilitate the training of agents to perform complex tasks in highly diverse environments.

Conclusions and Future Work

D3IL has been shown to be a promising approach to domain-adaptive IL with visual observations, efficiently addressing domain shifts. Its key advantages stem from successfully retaining domain-independent behavioral information within feature vectors through improved extraction methods. While effective, the current methodology is complex and requires fine-tuning of several loss functions.

Future work might focus on simplifying the tuning process, updating the feature extraction model with the learner’s experiences, and exploring offline domain-adaptive IL approaches. Another avenue for research could be extending the method to quantify domain shifts, enabling assessment of task difficulty and tackling more complex IL problems. Further exploration might include investigating multi-task or multi-modal learning scenarios.