We introduce Rotateit, a system that enables fingertip-based object rotation along multiple
axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it …

In this work, we tackle the problem of learning universal robotic dexterous grasping from a
point cloud observation under a table-top setting. The goal is to grasp and lift up objects in …

We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …

Vision-based teleoperation offers the possibility to endow robots with human-level
intelligence to physically interact with the environment, while only requiring low-cost camera …

Tactile information plays a critical role in human dexterity. It reveals useful contact
information that may not be inferred directly from vision. In fact, humans can even perform in …

Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual
feedback. However, the distinct nature of these modalities poses significant challenges. In …

Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots
with bimanual dexterous hands remains a challenge. Existing teleoperation systems …

Human hands possess the dexterity to interact with diverse objects such as grasping specific
parts of the objects and/or approaching them from desired directions. More importantly …

We present ArtiGrasp, a novel method to synthesize bimanual hand-object interactions that
include grasping and articulation. This task is challenging due to the diversity of the global …

We present Im2Flow2Act, a scalable learning framework that enables robots to acquire real-
world manipulation skills without the need of real-world robot training data. The key idea …