This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

Both text and video data are abundant on the internet and support large-scale self-
supervised learning through next token or frame prediction. However, they have not been …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …

Large text-to-video models hold immense potential for a wide range of downstream
applications. However, these models struggle to accurately depict dynamic object …

Predicting temporal progress from visual trajectories is important for intelligent robots that
can learn, adapt, and improve. However, learning such progress estimator, or temporal …

Large Language Models (LLMs) have the potential to automate reward engineering by
leveraging their broad domain knowledge across various tasks. However, they often need …

Task specification for robotic manipulation in open-world environments is challenging,
requiring flexible and adaptive objectives that align with human intentions and can evolve …

In recent years, reinforcement learning (RL)-based methods for learning driving policies
have gained increasing attention in the autonomous driving community and have achieved …

Tight coordination is required for effective human-robot teams in domains involving fast
dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates …

Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid
meticulous reward engineering by learning rewards based on human preferences …