133 citations · 676 across the 41 of their papers we have counts for
41 papers
MATE: Asynchronous Multi-Rate Diffusion Policy Composition
Zihao He, Hongjie Fang, Shirun Tang +2
Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance. However,…
Dense Policy: Bidirectional Autoregressive Learning of Actions
Yue Su, Xinyu Zhan, Hongjie Fang +5
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, ha…
AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons
Hongjie Fang, Chenxi Wang, Yiming Wang +11
Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on…
AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
Hao-Shu Fang, Hengxu Yan, Zhenyu Tang +3
We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional…
Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge
Hengxu Yan, Haoshu Fang, Cewu Lu
Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the su…
FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
Zihao He, Hongjie Fang, Jingjing Chen +2
Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies o…
CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation
Shangning Xia, Hongjie Fang, Cewu Lu +1
Generalization in robotic manipulation remains a critical challenge, particularly when scaling to new environments with limited demonstrations. This paper introduces CAGE, a novel…
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
Jingjing Chen, Hongjie Fang, Hao-Shu Fang +1
Data is crucial for robotic manipulation, as it underpins the development of robotic systems for complex tasks. While high-quality, diverse datasets enhance the performance and ada…
A Surprisingly Efficient Representation for Multi-Finger Grasping
Hengxu Yan, Hao-Shu Fang, Cewu Lu
The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar…
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
Chenxi Wang, Hao-Shu Fang, Minghao Gou +3
Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt…
RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective
Chenxi Wang, Hongjie Fang, Hao-Shu Fang +1
Precise robot manipulations require rich spatial information in imitation learning. Image-based policies model object positions from fixed cameras, which are sensitive to camera vi…
RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents
Zeren Chen, Zhelun Shi, Xiaoya Lu +8
Achieving generalizability in solving out-of-distribution tasks is one of the ultimate goals of learning robotic manipulation. Recent progress of Vision-Language Models (VLMs) has…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild
Hongjie Fang, Hao-Shu Fang, Yiming Wang +5
While humans can use parts of their arms other than the hands for manipulations like gathering and supporting, whether robots can effectively learn and perform the same type of ope…
Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory Generation
Gu Zhang, Hao-Shu Fang, Hongjie Fang +1
In recent years, there has been a significant effort dedicated to developing efficient, robust, and general human-to-robot handover systems. However, the area of flexible handover…
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
Hao-Shu Fang, Hongjie Fang, Zhenyu Tang +5
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown prom…
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains
Hao-Shu Fang, Chenxi Wang, Hongjie Fang +6
As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous acro…
Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions
Yong-Lu Li, Hongwei Fan, Zuoyu Qiu +8
Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting HOIs from videos, which is crucial for activity understanding. In daily HOIs, humans often interact wi…
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Hao-Shu Fang, Jiefeng Li, Hongyang Tang +5
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior…
X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360 Insufficient RGB-D Views
Haoyi Zhu, Hao-Shu Fang, Cewu Lu
Neural Radiance Fields (NeRFs), despite their outstanding performance on novel view synthesis, often need dense input views. Many papers train one model for each scene respectively…
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
Minghao Gou, Haolin Pan, Hao-Shu Fang +3
Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects tha…
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline
Hongjie Fang, Hao-Shu Fang, Sheng Xu +1
Transparent objects are common in our daily life and frequently handled in the automated production line. Robust vision-based robotic grasping and manipulation for these objects wo…
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu +6
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been ad…
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Shoubin Yu, Zhongyin Zhao, Haoshu Fang +6
Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize…