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 …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

We introduce Diffusion World Model (DWM), a conditional diffusion model capable of
predicting multistep future states and rewards concurrently. As opposed to traditional one …

In this study, we explore the influence of different observation spaces on robot learning,
focusing on three predominant modalities: RGB, RGB-D, and point cloud. Through extensive …

Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical
operations such as manipulation and grasping. Traditional methods for achieving this …

In recent years, the integration of large language models (LLMs) has revolutionized the field
of robotics, enabling robots to communicate, understand, and reason with human-like …

Temporal abstraction and efficient planning pose significant challenges in offline
reinforcement learning, mainly when dealing with domains that involve temporally extended …

Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of
scene understanding and action prediction. Current methods employ both 3D representation …

Humans possess a remarkable talent for flexibly alternating to different senses when
interacting with the environment. Picture a chef skillfully gauging the timing of ingredient …

We aim to discover manipulation concepts embedded in the unannotated demonstrations,
which are recognized as key physical states. The discovered concepts can facilitate training …