Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

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 …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

To achieve human-level dexterity, robots must infer spatial awareness from multimodal
sensing to reason over contact interactions. During in-hand manipulation of novel objects …

Human dexterity is an invaluable capability for precise manipulation of objects in complex
tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects …

Imitation learning provides an efficient way to teach robots dexterous skills; however,
learning complex skills robustly and generalizablely usually consumes large amounts of …

In-hand manipulation of pen-like objects is an important skill in our daily lives, as many tools
such as hammers and screwdrivers are similarly shaped. However, current learning-based …

We explore the dexterous manipulation transfer problem by designing simulators. The task
wishes to transfer human manipulations to dexterous robot hand simulations and is …

We present a method for controlling a simulated humanoid to grasp an object and move it to
follow an object trajectory. Due to the challenges in controlling a humanoid with dexterous …

In this work, we introduce the EyeSight Hand, a novel 7 degrees of freedom (DoF) humanoid
hand featuring integrated vision-based tactile sensors tailored for enhanced whole-hand …