Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

Abstract 3D bioprinting, a vital tool in tissue engineering, drug testing, and disease
modeling, is increasingly integrated with machine learning (ML) and artificial intelligence …

A key theme in the past decade has been that when large neural networks and large
datasets combine they can produce remarkable results. In deep reinforcement learning (RL) …

Learning rewards from expert videos offers an affordable and effective solution to specify the
intended behaviors for reinforcement learning (RL) tasks. In this work, we propose Diffusion …

Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …

Pre-training with offline data and online fine-tuning using reinforcement learning is a
promising strategy for learning control policies by leveraging the best of both worlds in terms …

Informational parsimony provides a useful inductive bias for learning representations that
achieve better generalization by being robust to noise and spurious correlations. We …

While bisimulation-based approaches hold promise for learning robust state representations
for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up to …