The field of robot learning is moving towards developing more robust and generalizable policies. Recent research has focused on addressing the challenges of imbalanced datasets, limited expert data, and the need for more efficient exploration strategies. One of the key directions is the use of offline reinforcement learning and data augmentation techniques to improve the performance of imitation learning policies. Additionally, researchers are exploring new sampling strategies and algorithmic modifications to effectively utilize non-expert data and mitigate the effects of dataset imbalance. Noteworthy papers include: OffSim, which proposes a novel model-based offline inverse reinforcement learning framework to emulate environmental dynamics and reward structure. RESample, which introduces a robust data augmentation framework via exploratory sampling for robotic manipulation. Using Temperature Sampling to Effectively Train Robot Learning Policies on Imbalanced Datasets, which proposes a simple sampling strategy to mitigate dataset imbalance. Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning, which demonstrates the effectiveness of offline reinforcement learning in harnessing non-expert data to enhance imitation learning policies.