204 citations · 214 across the 14 of their papers we have counts for
14 papers
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
Kevin Lin, Ajay Mandlekar, Caelan Reed Garrett +7
Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and…
EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
Ruijie Zheng, Dantong Niu, Yuqi Xie +12
Human behavior is among the most scalable sources of data for learning physical intelligence, yet how to effectively leverage it for dexterous manipulation remains unclear. While p…
World Action Models are Zero-shot Policies
Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng +33
State-of-the-art Vision-Language-Action (VLA) models excel at semantic generalization but struggle to generalize to unseen physical motions in novel environments. We introduce Drea…
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Wenli Xiao, Haotian Lin, Andy Peng +9
Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits sc…
SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
Yu Zhang, Yuqi Xie, Huihan Liu +4
Imitation learning advances robot capabilities by enabling the acquisition of diverse behaviors from human demonstrations. However, large-scale datasets used for policy training of…
Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation
Abhiram Maddukuri, Zhenyu Jiang, Lawrence Yunliang Chen +12
Large real-world robot datasets hold great potential to train generalist robot models, but scaling real-world human data collection is time-consuming and resource-intensive. Simula…
Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers
Jake Grigsby, Yuqi Xie, Justin Sasek +2
Competitive Pokémon Singles (CPS) is a popular strategy game where players learn to exploit their opponent based on imperfect information in battles that can last more than one hun…
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
NVIDIA, :, Johan Bjorck +40
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…
Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions
Zhenyu Jiang, Yuqi Xie, Jinhan Li +3
Humanoid robots, with their human-like embodiment, have the potential to integrate seamlessly into human environments. Critical to their coexistence and cooperation with humans is…
OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation
Jinhan Li, Yifeng Zhu, Yuqi Xie +4
We study the problem of teaching humanoid robots manipulation skills by imitating from single video demonstrations. We introduce OKAMI, a method that generates a manipulation plan…
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…
Voyager: An Open-Ended Embodied Agent with Large Language Models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang +5
We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries…