Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL - Robot Learning: Scaling Behavoir Cloning

This lecture discusses the challenges of imitation learning, specifically the problem of training on larger datasets and addressing distribution shifts. It explains that traditional behaviour cloning methods, where an agent learns to mimic expert demonstrations, can suffer from performance degradation when the agent encounters situations not present in the training data. This occurs due to the discrepancy between the distribution of states observed during training and the actual states encountered in the real world. To address this, techniques such as diffusion models are employed to capture more complex distributions of expert behaviour, going beyond simple Gaussian distributions. This approach allows the agent to develop a more nuanced understanding of the task and potentially enhance its generalization capabilities. The importance of data diversity and quality is emphasized in training, highlighting the need for data collected from multiple experts to improve the robustness and generalizability of the learned behaviour.