20 citations · 28 across the 15 of their papers we have counts for
15 papers
ViTacGen: Robotic Pushing with Vision-to-Touch Generation
Zhiyuan Wu, Yijiong Lin, Yongqiang Zhao +4
Robotic pushing is a fundamental manipulation task that requires tactile feedback to capture subtle contact forces and dynamics between the end-effector and the object. However, re…
NeuralTouch: Neural Descriptors for Precise Sim-to-Real Tactile Robot Control
Yijiong Lin, Bowen Deng, Keju Pu +4
Grasping accuracy is a critical prerequisite for precise object manipulation, often requiring careful alignment between the robot hand and object. Neural Descriptor Fields (NDF) of…
Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Harrison Field, Max Yang, Yijiong Lin +3
Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critica…
Classification of Vision-Based Tactile Sensors: A Review
Haoran Li, Yijiong Lin, Chenghua Lu +3
Vision-based tactile sensors (VBTS) have gained widespread application in robotic hands, grippers and prosthetics due to their high spatial resolution, low manufacturing costs, and…
SimShear: Sim-to-Real Shear-based Tactile Servoing
Kipp McAdam Freud, Yijiong Lin, Nathan F. Lepora
We present SimShear, a sim-to-real pipeline for tactile control that enables the use of shear information without explicitly modeling shear dynamics in simulation. Shear, arising f…
Tactile SoftHand-A: 3D-Printed, Tactile, Highly-underactuated, Anthropomorphic Robot Hand with an Antagonistic Tendon Mechanism
Haoran Li, Christopher J. Ford, Chenghua Lu +5
A challenging and important problem for tendon-driven multi-fingered robotic hands is to ensure grasping adaptivity while minimizing the number of actuators needed to provide human…
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…
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…
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…
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…
Probabilistic Discriminative Models Address the Tactile Perceptual Aliasing Problem
John Lloyd, Yijiong Lin, Nathan F. Lepora
In this paper, our aim is to highlight Tactile Perceptual Aliasing as a problem when using deep neural networks and other discriminative models. Perceptual aliasing will arise wher…