We propose a hybrid model predictive control algorithm, consensus complementarity
control, for systems that make and break contact with their environment. Many state-of-the …

Using tactile sensors for manipulation remains one of the most challenging problems in
robotics. At the heart of these challenges is generalization: How can we train a tactile-based …

Generalizable manipulation requires that robots be able to interact with novel objects and
environment. This requirement makes manipulation extremely challenging as a robot has to …

We introduce a novel approach that combines tactile estimation and control for in-hand
object manipulation. By integrating measurements from robot kinematics and an image …

Contacts play a critical role in most manipulation tasks. Robots today mainly use proximal
touch/force sensors to sense contacts, but the information they provide must be calibrated …

The 3-D motion field on the surface of the vision-based tactile sensor contains rich tactile
information and serves as the foundation for many downstream tasks. However, achieving …

We introduce a novel approach that combines tactile estimation and control for in-hand
object manipulation. By integrating measurements from robot kinematics and an image …

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

In this paper, we consider the problem of nonprehensile manipulation using grasped
objects. This problem is a superset of many common manipulation skills including instances …

This paper addresses a fundamental problem in multi-finger robot dexterous manipulation,
Multi-Tactile Sensor Calibration (MTSC). It involves estimating the relative poses between …