Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …

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 …

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

We present RobotGPT, an innovative decision framework for robotic manipulation that
prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee …

Developing intelligent robots for complex manipulation tasks in household and factory
settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the …

We introduce CyberDemo a novel approach to robotic imitation learning that leverages
simulated human demonstrations for real-world tasks. By incorporating extensive data …

To realize effective large-scale, real-world robotic applications, we must evaluate how well
our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of …

The ability to reuse collected data and transfer trained policies between robots could
alleviate the burden of additional data collection and training. While existing approaches …

Robot training often takes place in simulated environments, particularly with reinforcement
learning. Therefore, multiple training environments are generated using domain …

Introduction Robotic assembly tasks require precise manipulation and coordination, often
necessitating advanced learning techniques to achieve efficient and effective performance …