We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

Robotic applications often involve working in environments that are uncertain, dynamic, and
partially observable. Recently, diffusion models have been proposed for learning trajectory …

Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …

In this work, we study the problem of how to leverage instructional videos to facilitate the
understanding of human decision-making processes, focusing on training a model with the …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …

The explainability of reinforcement learning (RL) models has received vast amount of
interest as its applications have widened. Most existing explainable RL models focus on …

Autonomous driving systems have a pipeline of perception, decision, planning, and control.
The decision module processes information from the perception module and directs the …

Although the issue of sparse expert samples at the early stage of training in inverse
reinforcement learning (IRL) is successfully resolved by the introduction of generative …

In this work, we consider one-shot imitation learning for object rearrangement tasks, where
an AI agent needs to watch a single expert demonstration and learn to perform the same …