1 citations · 1 across the 5 of their papers we have counts for
6 papers
DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity
Weikang Wan, Zeyu Lai, Jianyu Hou +4
Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisions that these commands rarely…
Scaling Cross-Embodiment World Models for Dexterous Manipulation
Zihao He, Bo Ai, Tongzhou Mu +6
Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data shar…
LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
Weikang Wan, Jiawei Fu, Xiaodi Yuan +2
Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity an…
ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making
Liu Dai, Haina Wang, Weikang Wan +1
Building embodied agents capable of accomplishing arbitrary tasks is a core objective towards achieving embodied artificial general intelligence (E-AGI). While recent work has adva…
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
Zhenyu Jiang, Yuqi Xie, Kevin Lin +5
Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more br…
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery
Weikang Wan, Yifeng Zhu, Rutav Shah +1
We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks throughout its lif…