17 citations · 56 across the 23 of their papers we have counts for
24 papers
TouchBridge: Tactile-Conditioned Human-to-Robot Transfer for Dexterous Manipulation
Haozhe Zhang, Gu Zhang, Zirui Zou +7
Human demonstrations offer an efficient and scalable source of data for dexterous robot manipulation, but differences in sensing and hand morphology complicate the transfer of cont…
MemoryWAM: Efficient World Action Modeling with Persistent Memory
Sizhe Yang, Juncheng Mu, Tianming Wei +8
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling. World action models (WAMs) posse…
UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos
Gu Zhang, Qicheng Xu, Haozhe Zhang +16
Dexterous manipulation remains challenging due to the cost of collecting real-robot teleoperation data, the heterogeneity of hand embodiments, and the high dimensionality of contro…
ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning
Yufeng Tian, Shuiqi Cheng, Tianming Wei +6
Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly foc…
HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
Zhecheng Yuan, Tianming Wei, Langzhe Gu +4
Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source…
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo +3
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early…
HDP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
Yiyang Lu, Yufeng Tian, Zhecheng Yuan +4
Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distrib…
DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning
Zhengrong Xue, Shuying Deng, Zhenyang Chen +3
Visuomotor policies have shown great promise in robotic manipulation but often require substantial amounts of human-collected data for effective performance. A key reason underlyin…
DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
Han Zhang, Songbo Hu, Zhecheng Yuan +1
Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive e…
DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo
Junzhe Zhu, Yuanchen Ju, Junyi Zhang +4
Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Co…
Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng +3
Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose \textbf{Maniwhere}, a generalizable framework…
RoboDuet: Learning a Cooperative Policy for Whole-body Legged Loco-Manipulation
Guoping Pan, Qingwei Ben, Zhecheng Yuan +6
Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the…
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
Junting Chen, Yao Mu, Qiaojun Yu +11
Rapid progress in high-level task planning and code generation for open-world robot manipulation has been witnessed in Embodied AI. However, previous studies put much effort into g…
Generalizable Visual Reinforcement Learning with Segment Anything Model
Ziyu Wang, Yanjie Ze, Yifei Sun +2
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL). While most current methods focus on acquiring robust…
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation
Yanjie Ze, Yuyao Liu, Ruizhe Shi +4
Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human andfor…
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Guowei Xu, Ruijie Zheng, Yongyuan Liang +12
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the…
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Lirui Wang, Yiyang Ling, Zhecheng Yuan +6
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, exi…
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Zhecheng Yuan, Sizhe Yang, Pu Hua +3
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization…
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan +4
Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust represe…
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Nicklas Hansen, Zhecheng Yuan, Yanjie Ze +5
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks. We revisit a simple Learning-from-Scratch (LfS) baseline that incorporates data augmentat…
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Guozheng Ma, Zhen Wang, Zhecheng Yuan +3
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains. However, deployin…
Extraneousness-Aware Imitation Learning
Ray Chen Zheng, Kaizhe Hu, Zhecheng Yuan +2
Visual imitation learning provides an effective framework to learn skills from demonstrations. However, the quality of the provided demonstrations usually significantly affects the…
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Zhengrong Xue, Zhecheng Yuan, Jiashun Wang +3
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance? In this paper, we try to…
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
Zhecheng Yuan, Guozheng Ma, Yao Mu +5
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at en…