Improved Continual Imitation Learning via Generative and Prediction Models

In this paper, the authors propose a new method called Trajectory-based Deep Generative Replay (t-DGR) to improve the performance of continual world models in learning and forgetting tasks. The key idea is to use trajectories from previous tasks to guide the generation of new tasks, rather than relying solely on knowledge from the previous task. This approach helps the model to learn more efficiently and avoid catastrophic forgetting.
The authors start by explaining that traditional regularization methods for continual learning rely on knowledge of task endpoints to apply techniques effectively. However, this can lead to blurry task boundaries, making it difficult for the model to learn new tasks while avoiding forgetting old ones. To address this issue, t-DGR uses a combination of trajectories from previous tasks and knowledge from the previous task to guide the generation of new tasks.
The paper then delves into the technical details of t-DGR, including how it initializes the dataset, generates trajectories, and updates the generator and learner. The authors also discuss the choice of hyperparameters and their impact on the model’s performance.
To evaluate the effectiveness of t-DGR, the authors conduct experiments on several benchmark tasks and compare its performance to other state-of-the-art methods. The results show that t-DGR outperforms other methods in terms of both learning rate and forgetting rate. Specifically, t-DGR achieves a higher success rate in completing tasks while minimizing the loss of previous knowledge.
The authors also analyze the contribution of different components of t-DGR to its overall performance. They find that the use of trajectories from previous tasks is crucial for guiding the generation of new tasks and avoiding forgetting. Additionally, they show that the choice of replay ratio, which determines how much of the dataset to reuse from previous tasks, has a significant impact on performance.
In conclusion, t-DGR offers a promising approach to continual learning in robotics by leveraging trajectories from previous tasks to guide the generation of new tasks. By avoiding catastrophic forgetting and improving learning efficiency, t-DGR can help robots adapt to changing environments while preserving their knowledge and skills.