Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

We show that large language models (LLMs) can be adapted to be generalizable policies
for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement …

Humans use different modalities, such as speech, text, images, videos, etc., to communicate
their intent and goals with teammates. For robots to become better assistants, we aim to …

The ability to learn and refine behavior after deployment has become ever more important
for robots as we design them to operate in unstructured environments like households. In …

This review paper provides a comprehensive analysis of over 100 research papers focused
on the challenges of robotic grasping and the effectiveness of various machine learning …

Learning to navigate to an image-specified goal is an important but challenging task for
autonomous systems like household robots. The agent is required to well understand and …

The main challenge in learning image-conditioned robotic policies is acquiring a visual
representation conducive to low-level control. Due to the high dimensionality of the image …

For intelligent agents (eg robots) to be seamlessly integrated into human society, humans
must be able to understand their decision making. For example, the decision making of …

People often learn categories through interaction with knowledgeable others who may use
verbal explanations, visual exemplars, or both, to share their knowledge. Verbal and …