115 citations · 120 across the 11 of their papers we have counts for
11 papers
AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch
Max Yang, Chenghua Lu, Alex Church +6
Human hands are capable of in-hand manipulation in the presence of different hand motions. For a robot hand, harnessing rich tactile information to achieve this level of dexterity…
TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing
Mauro Comi, Yijiong Lin, Alex Church +3
Humans rely on their visual and tactile senses to develop a comprehensive 3D understanding of their physical environment. Recently, there has been a growing interest in exploring a…
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control
Yijiong Lin, Mauro Comi, Alex Church +2
High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environmen…
Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing
Max Yang, Yijiong Lin, Alex Church +4
Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods ha…
Bi-Touch: Bimanual Tactile Manipulation with Sim-to-Real Deep Reinforcement Learning
Yijiong Lin, Alex Church, Max Yang +4
Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availabilit…
Semi-Supervised Disentanglement of Tactile Contact~Geometry from Sliding-Induced Shear
Anupam K. Gupta, Alex Church, Nathan F. Lepora
The sense of touch is fundamental to human dexterity. When mimicked in robotic touch, particularly by use of soft optical tactile sensors, it suffers from distortion due to motion-…
Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch
Yijiong Lin, John Lloyd, Alex Church +1
High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-envi…
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation
Alex Church, John Lloyd, Raia Hadsell +1
Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs. Tac…
Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard
Alex Church, John Lloyd, Raia Hadsell +1
Artificial touch would seem well-suited for Reinforcement Learning (RL), since both paradigms rely on interaction with an environment. Here we propose a new environment and set of…
Tactile Model O: Fabrication and testing of a 3d-printed, three-fingered tactile robot hand
Jasper W. James, Alex Church, Luke Cramphorn +1
Bringing tactile sensation to robotic hands will allow for more effective grasping, along with the wide range of benefits of human-like touch. Here we present a 3D-printed, three-f…
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Nathan F. Lepora, Alex Church, Conrad De Kerckhove +2
Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniqu…