115 citations · 379 across the 65 of their papers we have counts for
7 papers · 2 filters
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…
DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch
Nathan F. Lepora, Yijiong Lin, Ben Money-Coomes +1
Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and experti…
Mapping Mid-air Haptics with a Low-cost Tactile Robot
Noor Alakhawand, William Frier, Nathan F. Lepora
Mid-air haptics create a new mode of feedback to allow people to feel tactile sensations in the air. Ultrasonic arrays focus acoustic radiation pressure in space, to induce tactile…
BRL/Pisa/IIT SoftHand: A Low-cost, 3D-Printed, Underactuated, Tendon-Driven Hand with Soft and Adaptive Synergies
Haoran Li, Christopher J. Ford, Matteo Bianchi +3
This paper introduces the BRL/Pisa/IIT (BPI) SoftHand: a single actuator-driven, low-cost, 3D-printed, tendon-driven, underactuated robot hand that can be used to perform a range o…
One-Shot Domain-Adaptive Imitation Learning via Progressive Learning
Dandan Zhang, Wen Fan, John Lloyd +2
Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to ad…