Search Results for author:
Found 6 papers, 2 papers with code
AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch
In the real world, we demonstrate successful sim-to-real transfer of the dense tactile policy, generalizing to a diverse range of objects for various rotation axes and hand directions and outperforming other forms of low-dimensional touch.
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces
Touch and vision go hand in hand, mutually enhancing our ability to understand the world.
TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing
To address these challenges, we propose TouchSDF, a Deep Learning approach for tactile 3D shape reconstruction that leverages the rich information provided by a vision-based tactile sensor and the expressivity of the implicit neural representation DeepSDF.
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control
To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision.
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements.
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
Our work demonstrates that invariant transformations on RL trajectories are a promising methodology to speed up learning in deep RL.
