Recent progress in imitation learning from human demonstrations has shown promising
results in teaching robots manipulation skills. To further scale up training datasets, recent …

Humanoid robots capable of autonomous operation in diverse environments have long
been a goal for roboticists. However, autonomous manipulation by humanoid robots has …

In this work, we aim to learn a unified vision-based policy for multi-fingered robot hands to
manipulate a variety of objects in diverse poses. Though prior work has shown benefits of …

The scale and diversity of demonstration data required for imitation learning is a significant
challenge. We present EgoMimic, a full-stack framework which scales manipulation via …

Imitation learning has demonstrated significant potential in performing high-precision
manipulation tasks using visual feedback from cameras. However, it is common practice in …

In most contact-rich manipulation tasks, humans apply time-varying forces to the target
object, compensating for inaccuracies in the vision-guided hand trajectory. However, current …

Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich
tasks. However, the high dimensionality of tactile data, due to the large coverage on …

Training robots directly from human videos is an emerging area in robotics and computer
vision. While there has been notable progress with two-fingered grippers, learning …

Imitation learning requires high-quality demonstrations consisting of sequences of state-
action pairs. For contact-rich dexterous manipulation tasks that require fine-grained …

We introduce a teleoperation system that integrates a 5 DOF actuated neck, designed to
replicate natural human head movements and perception. By enabling behaviors like …