Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Soft robots have been extensively researched due to their flexible, deformable, and adaptive
characteristics. However, compared to rigid robots, soft robots have issues in modeling …

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 …

Despite decades of research, general purpose in-hand manipulation remains one of the
unsolved challenges of robotics. One of the contributing factors that limit current robotic …

Universal picking (UP), or reliable robot gras** of a diverse range of novel objects from
heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and …

The ability to associate touch with other modalities has huge implications for humans and
computational systems. However multimodal learning with touch remains challenging due to …

Tactile sensing is a key sensor modality for robots interacting with their surroundings. These
sensors provide a rich and diverse set of data signals that contain detailed information …

Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. However, it is non-trivial to manually design a robot controller that …

Cables are complex, high-dimensional, and dynamic objects. Standard approaches to
manipulate them often rely on conservative strategies that involve long series of very slow …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …