7 citations · 24 across the 13 of their papers we have counts for
5 papers · 1 filter
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
Lirui Wang, Xinlei Chen, Jialiang Zhao +1
One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for on…
Learning Object Compliance via Young's Modulus from Single Grasps using Camera-Based Tactile Sensors
Michael Burgess, Jialiang Zhao, Laurence Willemet
Compliance is a useful parametrization of tactile information that humans often utilize in manipulation tasks. It can be used to inform low-level contact-rich actions or characteri…
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks
Jialiang Zhao, Yuxiang Ma, Lirui Wang +1
This paper presents T3: Transferable Tactile Transformers, a framework for tactile representation learning that scales across multi-sensors and multi-tasks. T3 is designed to overc…
GelLink: A Compact Multi-phalanx Finger with Vision-based Tactile Sensing and Proprioception
Yuxiang Ma, Jialiang Zhao, Edward Adelson
Compared to fully-actuated robotic end-effectors, underactuated ones are generally more adaptive, robust, and cost-effective. However, state estimation for underactuated hands is u…
PoCo: Policy Composition from and for Heterogeneous Robot Learning
Lirui Wang, Jialiang Zhao, Yilun Du +2
Training general robotic policies from heterogeneous data for different tasks is a significant challenge. Existing robotic datasets vary in different modalities such as color, dept…