5 citations · 7 across the 6 of their papers we have counts for
6 papers
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces
Mauro Comi, Alessio Tonioni, Max Yang +5
Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and p…
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…
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…