20 citations · 24 across the 9 of their papers we have counts for
9 papers
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…
DexiTac: Soft Dexterous Tactile Gripping
Chenghua Lu, Kailuan Tang, Max Yang +2
Grasping object,whether they are flat, round, or narrow and whether they have regular or irregular shapes,introduces difficulties in determining the ideal grasping posture, even fo…
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…
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…
Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing
Wen Fan, Max Yang, Yifan Xing +2
Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to h…