Reproducing the capabilities of the human sense of touch in machines is an important step
in enabling robot manipulation to have the ease of human dexterity. A combination of robotic …

A vision-based tactile sensor (VBTS) is an innovative optical sensor widely applied in robotic
perception. The VBTS consists of a contact module, an illumination module, and a camera …

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 …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

Vision-based haptic sensors have emerged as a promising approach to robotic touch due to
affordable high-resolution cameras and successful computer vision techniques; however …

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 …

This work presents a new version of tactile-sensing finger, GelSlim 3.0, which integrates the
ability to sense high-resolution shape, force, and slip in a more compact form factor than …

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 …

We propose a sim-to-real framework for dexterous manipulation which can generalize to
new objects of the same category in the real world. The key of our framework is to train the …