10 citations · 21 across the 23 of their papers we have counts for
24 papers
When Does Legacy Data Start to Help? Emergent Transfer in Cross-Configuration Robot Learning
Tao Wang, Hudson Hou, Yingdong Hu +7
Robotic hardware evolves over time, but demonstration data is often tied to a specific sensor and actuator configuration. This raises a practical and underexplored question: when d…
OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
Yingdong Hu, Haodong Zhu, Boyuan Zheng +6
Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into se…
FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
Chengbo Yuan, Zicheng Zhang, Mingjie Zhou +14
Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals…
A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
Shuo Wang, Hanyuan Xu, Yingdong Hu +2
Simulation has become an essential tool for evaluating and improving vision-language-action (VLA) policies, offering scalable, reproducible, and controllable alternatives to costly…
Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
Ruiqian Nai, Boyuan Zheng, Junming Zhao +8
Current approaches for humanoid whole-body manipulation, primarily relying on teleoperation or visual sim-to-real reinforcement learning, are hindered by hardware logistics and com…
Learning Native Continuation for Action Chunking Flow Policies
Yufeng Liu, Hang Yu, Juntu Zhao +9
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking…
Point What You Mean: Visually Grounded Instruction Policy
Hang Yu, Juntu Zhao, Yufeng Liu +9
Vision-Language-Action (VLA) models align vision and language with embodied control, but their object referring ability remains limited when relying solely on text prompt, especial…
Best of Sim and Real: Decoupled Visuomotor Manipulation via Learning Control in Simulation and Perception in Real
Jialei Huang, Zhaoheng Yin, Yingdong Hu +3
Sim-to-real transfer remains a fundamental challenge in robot manipulation due to the entanglement of perception and control in end-to-end learning. We present a decoupled framewor…
Do You Need Proprioceptive States in Visuomotor Policies?
Juntu Zhao, Wenbo Lu, Di Zhang +10
Imitation-learning-based visuomotor policies have been widely used in robot manipulation, where both visual observations and proprioceptive states are typically adopted together fo…
MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
Chengbo Yuan, Rui Zhou, Mengzhen Liu +6
Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as ima…
GR-3 Technical Report
Chilam Cheang, Sijin Chen, Zhongren Cui +18
We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptiona…
OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning
Fanqi Lin, Ruiqian Nai, Yingdong Hu +3
General-purpose robots capable of performing diverse tasks require synergistic reasoning and acting capabilities. However, recent dual-system approaches, which separate high-level…
HuB: Learning Extreme Humanoid Balance
Tong Zhang, Boyuan Zheng, Ruiqian Nai +8
The human body demonstrates exceptional motor capabilities-such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters-both requiring precis…
Data Scaling Laws in Imitation Learning for Robotic Manipulation
Fanqi Lin, Yingdong Hu, Pingyue Sheng +3
Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investi…
Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation
Tong Zhang, Yingdong Hu, Jiacheng You +1
Given the high cost of collecting robotic data in the real world, sample efficiency is a consistently compelling pursuit in robotics. In this paper, we introduce SGRv2, an imitatio…
CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models
Haoxu Huang, Fanqi Lin, Yingdong Hu +2
Foundation models pre-trained on web-scale data are shown to encapsulate extensive world knowledge beneficial for robotic manipulation in the form of task planning. However, the ac…
Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
Yingdong Hu, Fanqi Lin, Tong Zhang +2
In this study, we are interested in imbuing robots with the capability of physically-grounded task planning. Recent advancements have shown that large language models (LLMs) posses…
Imitation Learning from Observation with Automatic Discount Scheduling
Yuyang Liu, Weijun Dong, Yingdong Hu +4
Humans often acquire new skills through observation and imitation. For robotic agents, learning from the plethora of unlabeled video demonstration data available on the Internet ne…
Policy Contrastive Imitation Learning
Jialei Huang, Zhaoheng Yin, Yingdong Hu +1
Adversarial imitation learning (AIL) is a popular method that has recently achieved much success. However, the performance of AIL is still unsatisfactory on the more challenging ta…
A Universal Semantic-Geometric Representation for Robotic Manipulation
Tong Zhang, Yingdong Hu, Hanchen Cui +2
Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing…
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Yingdong Hu, Renhao Wang, Li Erran Li +1
In recent years, increasing attention has been directed to leveraging pre-trained vision models for motor control. While existing works mainly emphasize the importance of this pre-…
Semantic-Aware Fine-Grained Correspondence
Yingdong Hu, Renhao Wang, Kaifeng Zhang +1
Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representati…
A Multi-channel Training Method Boost the Performance
Yingdong Hu
Deep convolutional neural network has made huge revolution and shown its superior performance on computer vision tasks such as classification and segmentation. Recent years, resear…
Adversarial Data Encryption
Yingdong Hu, Liang Zhang, Wei Shan +4
In the big data era, many organizations face the dilemma of data sharing. Regular data sharing is often necessary for human-centered discussion and communication, especially in med…