233 citations · 451 across the 42 of their papers we have counts for
7 papers · 1 filter
Whole-Body Dexterity
Conor Mc Gartoll, An Dang, Pranay Thangeda +5
Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the entire body by bracing and slidi…
TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning
Dayou Li, Hao Wang, Qianqian Yang +23
Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the contact and pressure that ch…
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Raymond Yu, William Huey, Mustafa Mukadam +2
Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL) is an appealing alternative, b…
PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
Rosy Chen, Mustafa Mukadam, Michael Kaess +4
Tactile dexterous manipulation is essential to automating complex household tasks, yet learning effective control policies remains a challenge. While recent work has relied on imit…
HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning
An Dang, Jayjun Lee, Mustafa Mukadam +4
In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image re…
TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment
Youngsun Wi, Jessica Yin, Elvis Xiang +5
Human demonstrations collected by wearable devices (e.g., tactile gloves) provide fast and dexterous supervision for policy learning, and are guided by rich, natural tactile feedba…
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…