We present Neural Contact Fields, a method that brings together neural fields and tactile
sensing to address the problem of tracking extrinsic contact between object and environment. …

Robotic manipulation tasks such as object insertion typically involve interactions between
object and environment, namely extrinsic contacts. Prior work on Neural Contact Fields (NCF) …

Simulating vision-based tactile sensors enables learning models for contact-rich tasks when
collecting real world data at scale can be prohibitive. However, modeling the optical …

We present TacX, the first multisensory touch representations across four tactile modalities:
image, audio, motion, and pressure. Trained on 1M contact-rich interactions collected with …

In this work, we introduce general purpose touch representations for the increasingly accessible
class of vision-based tactile sensors. Such sensors have led to many recent advances …

We present the application of multiagent reinforcement learning to the problem of traffic light
signal control to decrease travel time. We model roads as a collection of agents for each …

We present Sparsh-skin, a pre-trained encoder for magnetic skin sensors distributed across
the fingertips, phalanges, and palm of a dexterous robot hand. Magnetic tactile skins offer a …

We introduce multi-task Visuo-Tactile World Models (VT-WM), which capture the physics of
contact through touch reasoning. By complementing vision with tactile sensing, VT-WM better …

The increasing use of distributed and renewable energy resources presents a challenge for
traditional control methods due to the higher complexity and uncertainty brought by these …

In recent years, the cultivated area of hazelnuts in Chile has increased significantly. Along
with this rapid expansion, biotic constraints that affect the optimal development of the crop …