Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

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 …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

The burgeoning fields of robot learning and embodied AI have triggered an increasing
demand for large quantities of data. However, collecting sufficient unbiased data from the …

Reaching-and-grasping is a fundamental skill for robotic manipulation, but existing methods
usually train models on a specific gripper and cannot be reused on another gripper without …

Recent years in robotics and imitation learning have shown remarkable progress in training
large-scale foundation models by leveraging data across a multitude of embodiments. The …

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 …

Affordances, a concept rooted in ecological psychology and pioneered by James J. Gibson,
have emerged as a fundamental framework for understanding the dynamic relationship …

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 …