5 citations · 5 across the 5 of their papers we have counts for
6 papers
Visuo-Tactile World Models
Carolina Higuera, Sergio Arnaud, Byron Boots +3
We introduce multi-task Visuo-Tactile World Models (VT-WM), which capture the physics of contact through touch reasoning. By complementing vision with tactile sensing, VT-WM better…
SPIDER: Scalable Physics-Informed Dexterous Retargeting
Chaoyi Pan, Changhao Wang, Haozhi Qi +7
Learning dexterous and agile policy for humanoid and dexterous hand control requires large-scale demonstrations, but collecting robot-specific data is prohibitively expensive. In c…
Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation
Carolina Higuera, Akash Sharma, Taosha Fan +8
We present Sparsh-X, the first multisensory touch representations across four tactile modalities: image, audio, motion, and pressure. Trained on ~1M contact-rich interactions colle…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…
Self-supervised perception for tactile skin covered dexterous hands
Akash Sharma, Carolina Higuera, Chaithanya Krishna Bodduluri +9
We present Sparsh-skin, a pre-trained encoder for magnetic skin sensors distributed across the fingertips, phalanges, and palm of a dexterous robot hand. Magnetic tactile skins off…
DexterityGen: Foundation Controller for Unprecedented Dexterity
Zhao-Heng Yin, Changhao Wang, Luis Pineda +11
Teaching robots dexterous manipulation skills, such as tool use, presents a significant challenge. Current approaches can be broadly categorized into two strategies: human teleoper…