EDBT 2026 Demo / reviewers in the wild / expert
Haoshu Fang
dblp:192/1388
· DBLP profile ↗
35ranked-venue papers
11as first author
25since 2021 · last 2025
0000-0002-0758-0293ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 33 · 10 first-author · 23 since 2021Graphics, computer vision, multimedia, augmented reality and games · 19 · 8 first-author · 9 since 2021Systems, architecture and hardware · 12 · 1 first-author · 12 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action TokenizersabstractIn this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more data than previous approaches. This extensive dataset enables our tokenizer to capture rich spatiotemporal dynamics, resulting in a model that not only accelerates inference but also generates smoother and more coherent action outputs. Once trained, the tokenizer can be seamlessly adapted to a wide range of downstream tasks in a zero-shot manner, from short-horizon reactive behaviors to long-horizon planning. A key finding of our work is that the domain gap between synthetic and real action trajectories is marginal, allowing us to effectively utilize a vast amount of synthetic data during training without compromising real-world performance. To validate our approach, we conducted extensive experiments in both simulated environments and on real robotic platforms. The results demonstrate that as the volume of synthetic trajectory data increases, the performance of our tokenizer on downstream tasks improves significantly-most notably, achieving up to a 30% higher success rate on two real-world tasks in long-horizon scenarios. These findings highlight the potential of our action tokenizer as a robust and scalable solution for real-time embodied intelligence systems, paving the way for more efficient and reliable robotic control in diverse application domains.Project website: https://xiaoxiao0406.github.io/vqvla.github.io Haoyi Zhu, Jiange Yang, Haoshu Fang, Tong He 0001 |
ICCV | 5 |
| 2025 | Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and OptimizationabstractData 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 adaptability of robotic manipulation policies, collecting extensive expert-level data is resource-intensive. Consequently, many current datasets suffer from quality inconsistencies due to operator variability, highlighting the need for methods to utilize mixed-quality data effectively. To mitigate these issues, we propose “Select Segments to Imitate” (S2I), a framework that selects and optimizes mixed-quality demonstration data at the segment level, while ensuring plug-and-play compatibility with existing robotic manipulation policies. The framework has three components: demonstration segmentation dividing origin data into meaningful segments, segment selection using contrastive learning to find high-quality segments, and trajectory optimization to refine suboptimal segments for better policy learning. We evaluate S2I through comprehensive experiments in simulation and real-world environments across six tasks, demonstrating that with only 3 expert demonstrations for reference, S2I can improve the performance of various downstream policies when trained with mixed-quality demonstrations. Project website: https://tonyfang.net/s2i/. Hongjie Fang, Haoshu Fang, Cewu Lu |
ICRA | 3 |
| 2025 | Cage: Causal Attention Enables Data-Efficient Generalizable Robotic ManipulationabstractGeneralization in robotic manipulation remains a critical challenge, particularly when scaling to new environments with limited demonstrations. This paper introduces CAGE, a novel robotic manipulation policy designed to overcome these generalization barriers by integrating the pretrained visual representation with causal attention mechanism. CAGE utilizes the powerful feature extraction capabilities of the vision foundation model DINOv2, combined with LoRA fine-tuning for robust environment understanding. The policy further employs a causal perceiver for effective token compression and a diffusion-based action head with attention to enhance task-specific fine-grained conditioning. With as few as 50 demonstrations from a single training environment, CAGE achieves robust generalization across diverse visual changes in objects, backgrounds, and viewpoints. Extensive experiments validate that CAGE significantly outperforms existing state-of-the-art RGB/RGB-D-based approaches in various manipulation tasks, especially under large distribution shifts. In similar environments, CAGE offers an average of 42 % increase in task completion rate. While all baselines fail in unseen environments, CAGE manages to obtain a 43 % completion rate and a 51 % success rate in average, marking a substantial advancement toward the practical deployment of robots in real-world settings. Project website: cage-policy.github.io. Shangning Xia, Hongjie Fang, Cewu Lu, Haoshu Fang |
ICRA | 4 |
| 2025 | Dexterous Manipulation Based on Prior Dexterous Grasp Pose KnowledgeabstractDexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the substantial degrees of freedom in hand movements. Nonetheless, these methods typically suffer from low efficiency and accuracy. In this work, we introduce a novel reinforcement learning approach that leverages prior dexterous grasp pose knowledge to enhance both efficiency and accuracy. Unlike previous work, they always make the robotic hand go with a fixed dexterous grasp pose, We decouple the manipulation process into two distinct phases: initially, we generate a dexterous grasp pose targeting the functional part of the object; after that, we employ reinforcement learning to comprehensively explore the environment. Our findings suggest that the majority of learning time is expended in identifying the appropriate initial position and selecting the optimal manipulation viewpoint. Experimental results demonstrate significant improvements in learning efficiency and success rates across four distinct tasks. Hengxu Yan, Haoshu Fang, Cewu Lu |
IROS | 2 |
| 2025 | LDexMM: Language-Guided Dexterous Multi-Task Manipulation with Reinforcement LearningabstractLanguage plays a crucial role in robotic manipulation, particularly in facilitating complex tasks. Previous work primarily focused on two-finger manipulation. However, leveraging language to guide reinforcement learning for dexterous hands remains a challenge due to their high degrees of freedom. In this work, we introduce a language-guided dexterous multi-task manipulation framework (LDexMM), which decomposes the problem into two distinct phases. First, we use language instructions to guide a segmentation model in generating a dexterous grasp pose for the functional part of the object. After establishing this initial grasp, reinforcement learning is employed to refine the grasp pose and complete the task. Simultaneously, language constraints are applied to focus the actions on the specified object. Our experiments demonstrate success rates of 31%, 40%, 49.2%, and 72.7% on 10, 7, 5, and 3 tasks, respectively, with a single model. Hengxu Yan, Junbo Wang 0004, Haoshu Fang, Qiaojun Yu, Cewu Lu |
IROS | 3 |
| 2024 | RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-ShotabstractA key challenge for robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent progress in one-shot imitation learning and robotic foundation models have shown promise in transferring trained policies to new tasks based on demonstrations. This feature is attractive for enabling robots to acquire new skills and improve their manipulative ability. However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance. In reality, there are many complex skills, some of which may even require both visual and tactile perception to solve. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception. To achieve this, we have collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world. Each sequence in the dataset includes visual, force, audio, and action information. Moreover, we also provide a corresponding human demonstration video and a language description for each robot sequence. We have invested significant efforts in calibrating all the sensors and ensuring a high-quality dataset. The dataset is made publicly available on our website: rh20t.github.io. Haoshu Fang, Hongjie Fang, Zhenyu Tang 0005, Jirong Liu, Chenxi Wang 0003, Junbo Wang 0004, Haoyi Zhu, Cewu Lu |
ICRA | 1 |
| 2024 | AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the WildabstractWhile 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 operations remains relatively unexplored. As these manipulations require joint-level control to regulate the complete poses of the robots, we develop AirExo, a low-cost, adaptable, and portable dual-arm exoskeleton, for teleoperation and demonstration collection. As collecting teleoperated data is expensive and time-consuming, we further leverage AirExo to collect cheap in-the-wild demonstrations at scale. Under our in-the-wild learning framework, we show that with only 3 minutes of the teleoperated demonstrations, augmented by diverse and extensive in-the-wild data collected by AirExo, robots can learn a policy that is comparable to or even better than one learned from teleoperated demonstrations lasting over 20 minutes. Experiments demonstrate that our approach enables the model to learn a more general and robust policy across the various stages of the task, enhancing the success rates in task completion even with the presence of disturbances. Project website: airexo.github.io. Hongjie Fang, Haoshu Fang, Jieji Ren, Ruo Zhang, Cewu Lu |
ICRA | 2 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 75 |
| 2024 | A Surprisingly Efficient Representation for Multi-Finger GraspingabstractThe 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 objects in real and cluttered environments. In this work, we propose a representation that can be effectively mapped to the multi-finger grasp space. Based on this representation, we develop a simple decision model that generates accurate grasp quality scores for different multi-finger grasp poses using only hundreds to thousands of training samples. We demonstrate that our representation performs well on a real robot and achieves a success rate of 78.64% after training with only 500 real-world grasp attempts and 87% with 4500 grasp attempts. Additionally, we achieve a success rate of 84.51% in a dynamic human-robot handover scenario using a multi-finger hand. Hengxu Yan, Haoshu Fang, Cewu Lu |
ICRA | 2 |
| 2024 | EyeSight Hand: Design of a Fully-Actuated Dexterous Robot Hand with Integrated Vision-Based Tactile Sensors and Compliant ActuationabstractIn this work, we introduce the EyeSight Hand, a 7 degrees of freedom (DoF) humanoid hand featuring integrated vision-based tactile sensors tailored for enhanced whole-hand manipulation. Additionally, we introduce an actuation scheme centered around quasi-direct drive actuation to achieve human-like strength and speed while ensuring robustness for large-scale data collection. We evaluate the EyeSight Hand on three challenging tasks: bottle opening, plasticine cutting, and plate pick and place, which require a blend of complex manipulation, tool use, and precise force application. Imitation learning models trained on these tasks, with a vision dropout strategy, showcase the benefits of tactile feedback in enhancing task success rates. Our results reveal that the integration of tactile sensing dramatically improves task performance, underscoring the critical role of tactile information in dexterous manipulation. Branden Romero, Haoshu Fang, Pulkit Agrawal 0001, Edward H. Adelson |
IROS | 2 |
| 2024 | RISE: 3D Perception Makes Real-World Robot Imitation Simple and EffectiveabstractPrecise robot manipulations require rich spatial information in imitation learning. Image-based policies model object positions from fixed cameras, which are sensitive to camera view changes. Policies utilizing 3D point clouds usually predict keyframes rather than continuous actions, posing difficulty in dynamic and contact-rich scenarios. To utilize 3D perception efficiently, we present RISE, an end-to-end baseline for real-world imitation learning, which predicts continuous actions directly from single-view point clouds. It compresses the point cloud to tokens with a sparse 3D encoder. After adding sparse positional encoding, the tokens are featurized using a transformer. Finally, the features are decoded into robot actions by a diffusion head. Trained with 50 demonstrations for each real-world task, RISE surpasses currently representative 2D and 3D policies by a large margin, showcasing significant advantages in both accuracy and efficiency. Experiments also demonstrate that RISE is more general and robust to environmental change compared with previous baselines. Project website: rise-policy.github.io. Chenxi Wang 0003, Hongjie Fang, Haoshu Fang, Cewu Lu |
IROS | 3 |
| 2024 | Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly DetectionabstractAnomaly detection in surveillance videos is challenging but important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise. However, pose-based methods lack an alternative dynamic representation akin to the explicit motion features, such as optical flow, employed by pixel-based methods. In this paper, a novel Motion Embedder (ME), a label-efficient scheme without extra annotation efforts, is proposed to provide a pose motion representation for the structured posed data from a probability perspective. Furthermore, a novel task-specific Spatial-Temporal Transformer (STT) is deployed for self-supervised pose sequence reconstruction. These two modules are then integrated into a unified framework for pose regularity learning, which is referred to as Motion Prior Regularity Learner (MoPRL). MoPRL achieves competitive results on multiple challenging datasets while minimizing computational costs. Extensive experiments validate the versatility of the proposed modules and provide insights for future research. Shoubin Yu, Zhongyin Zhao, Haoshu Fang, Andong Deng, Haisheng Su, Weihao Gan, Cewu Lu, Wei Wu 0021 |
IEEE Trans. Circuits Syst. Video Technol. | 3 |
| 2023 | Target-referenced Reactive Grasping for Dynamic ObjectsabstractReactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics. Current methods mainly focus on the temporal smoothness of the predicted grasp poses but few consider their semantic consistency. Consequently, the predicted grasps are not guaranteed to fall on the same part of the same object, especially in cluttered scenes. In this paper, we propose to solve reactive grasping in a target-referenced setting by tracking through generated grasp spaces. Given a targeted grasp pose on an object and detected grasp poses in a new observation, our method is composed of two stages: 1) discovering grasp pose correspondences through an attentional graph neural network and selecting the one with the highest similarity with respect to the target pose; 2) refining the selected grasp poses based on target and historical information. We evaluate our method on a large-scale benchmark GraspNet-1Billion. We also collect 30 scenes of dynamic objects for testing. The results suggest that our method outperforms other representative methods. Furthermore, our real robot experiments achieve an average success rate of over 80 percent. Code and demos are available at: https://graspnet.net/reactive. Jirong Liu, Ruo Zhang, Haoshu Fang, Minghao Gou, Hongjie Fang, Chenxi Wang 0003, Hengxu Yan, Cewu Lu |
CVPR | 3 |
| 2023 | Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory GenerationabstractIn 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 in the context of complex and continuous objects' motion remains relatively unexplored. In this work, we propose an approach for effective and robust flexible handover, which enables the robot to grasp moving objects with flexible motion trajectories with a high success rate. The key innovation of our approach is the generation of real-time robust grasp trajec-tories. We also design a future grasp prediction algorithm to enhance the system's adaptability to dynamic handover scenes. We conduct one-motion handover experiments and motion-continuous handover experiments on our novel benchmark that includes 31 diverse household objects. The system we have developed allows users to move and rotate objects in their hands within a relatively large range. The success rate of the robot grasping such moving objects is 78.15 % over the entire household object benchmark. Gu Zhang, Haoshu Fang, Hongjie Fang, Cewu Lu |
IROS | 2 |
| 2023 | InstaBoost++: Visual Coherence Principles for Unified 2D/3D Instance Level Data Augmentation
Jianhua Sun 0003, Haoshu Fang, Runzhong Wang, Minghao Gou, Cewu Lu |
Int. J. Comput. Vis. | 2 |
| 2023 | AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-TimeabstractAccurate 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 analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this article, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose. Haoshu Fang, Hongyang Tang, Chao Xu 0023, Haoyi Zhu, Yuliang Xiu, Yong-Lu Li 0001, Cewu Lu |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |
| 2023 | HAKE: A Knowledge Engine Foundation for Human Activity UnderstandingabstractHuman activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances with deep learning, it remains challenging. The object recognition-like solutions usually try to map pixels to semantics directly, but activity patterns are much different from object patterns, thus hindering another success. In this article, we propose a novel paradigm to reformulate this task in two-stage: first mapping pixels to an intermediate space spanned by atomic activity primitives, then programming detected primitives with interpretable logic rules to infer semantics. To afford a representative primitive space, we build a knowledge base including 26+ M primitive labels and logic rules from human priors or automatic discovering. Our framework, Human Activity Knowledge Engine (HAKE), exhibits superior generalization ability and performance upon canonical methods on challenging benchmarks. Code and data are available at http://hake-mvig.cn/. Yong-Lu Li 0001, Xinpeng Liu 0002, Yizhuo Li 0001, Zuoyu Qiu, Liang Xu 0012, Haoshu Fang, Cewu Lu |
IEEE Trans. Pattern Anal. Mach. Intell. | 8 |
| 2023 | AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal DomainsabstractAs 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 across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this article, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial–temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables dynamic grasp tracking. Our model can efficiently generate accurate, 7-DoF, dense, and temporally-smooth grasp poses and works robustly against large depth-sensing noise. Using AnyGrasp, we achieve a 93.3% success rate when clearing bins with over 300 unseen objects, which is on par with human subjects under controlled conditions. Over 900 mean-picks-per-hour is reported on a single-arm system. For dynamic grasping, we demonstrate catching swimming robot fish in the water. Haoshu Fang, Chenxi Wang 0003, Hongjie Fang, Minghao Gou, Jirong Liu, Hengxu Yan, Wenhai Liu, Yichen Xie 0002, Cewu Lu |
IEEE Trans. Robotics | 1 |
| 2022 | Correlation Field for Boosting 3D Object Detection in Structured ScenesabstractData augmentation is an efficient way to elevate 3D object detection performance. In this paper, we propose a simple but effective online crop-and-paste data augmentation pipeline for structured 3D point cloud scenes, named CorrelaBoost. Observing that 3D objects should have reasonable relative positions in a structured scene because of the objects' functionalities and natural relationships, we express this correlation as a kind of interactive force. An energy field called Correlation Field can be calculated correspondingly across the whole 3D space. According to the Correlation Field, we propose two data augmentation strategies to explore highly congruent positions that a designated object may be pasted to: 1) Category Consistent Exchanging and 2) Energy Optimized Transformation. We conduct exhaustive experiments on various popular benchmarks with different detection frameworks and the results illustrate that our method brings huge free-lunch improvement and significantly outperforms state-of-the-art approaches in terms of data augmentation. It is worth noting that the performance of VoteNet with [email protected] is improved by 7.7 on ScanNetV2 dataset and 5.0 on SUN RGB-D dataset. Our method is simple to implement and increases few computational overhead. Jianhua Sun 0003, Haoshu Fang, Xianghui Zhu, Cewu Lu |
AAAI | 2 |
| 2022 | Human Trajectory Prediction with Momentary ObservationabstractHuman trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as tracking losses and sudden events. However, the problem of trajectory pre-diction with limited observations has not drawn much at-tention in previous work. In this paper, we study a task named momentary trajectory prediction, which reduces the observed history from a long time sequence to an extreme situation of two frames, one frame for social and scene contexts and both frames for the velocity of agents. We perform a rigorous study of existing state-of-the-art approaches in this challenging setting on two widely used benchmarks. We further propose a unified feature extractor, along with a novel pre-training mechanism, to capture effective infor-mation within the momentary observation. Our extractor can be adopted in existing prediction models and substan-tially boost their performance of momentary trajectory pre-diction. We hope our work will pave the way for more re-sponsive, precise and robust prediction approaches, an important step toward real-world autonomous systems. Jianhua Sun 0003, Liang Chai, Haoshu Fang, Yong-Lu Li 0001, Cewu Lu |
CVPR | 4 |
| 2021 | DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction DetectionabstractRecent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection. To tackle the problems above, we propose a novel one-stage HOI detection approach DIRV in this paper, based on a new concept called interaction region for the HOI problem. Unlike previous methods, our approach concentrates on the densely sampled interaction regions across different scales for each human-object pair, so as to capture the subtle visual features that is most essential to the interaction. Moreover, in order to compensate for the detection flaws of a single interaction region, we introduce a novel voting strategy that makes full use of those overlapped interaction regions in place of conventional Non-Maximal Suppression (NMS). Extensive experiments on two popular benchmarks: V-COCO and HICO-DET show that our approach outperforms existing state-of-the-arts by a large margin with the highest inference speed and lightest network architecture. Our code is publicly available at www.github.com/MVIG-SJTU/DIRV. Haoshu Fang, Yichen Xie 0002, Dian Shao, Cewu Lu |
AAAI | 1 |
| 2021 | DecAug: Augmenting HOI Detection via DecompositionabstractHuman-object interaction (HOI) detection requires a large amount of annotated data. Current algorithms suffer from insufficient training samples and category imbalance within datasets. To increase data efficiency, in this paper, we propose an efficient and effective data augmentation method called DecAug for HOI detection. Based on our proposed object state similarity metric, object patterns across different HOIs are shared to augment local object appearance features without changing their states. Further, we shift spatial correlation between humans and objects to other feasible configurations with the aid of a pose-guided Gaussian Mixture Model while preserving their interactions. Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements on V-COCO and HICO-DET dataset for two advanced models. Specifically, interactions with fewer samples enjoy more notable improvement. Our method can be easily integrated into various HOI detection models with negligible extra computational consumption. Haoshu Fang, Yichen Xie 0002, Dian Shao, Yong-Lu Li 0001, Cewu Lu |
AAAI | 1 |
| 2021 | Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and SynthesisabstractMultimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future. Previous frameworks can be divided into three categories: regression, generation and classification frameworks. However, these frameworks have weaknesses in different aspects so that they cannot model the multimodal prediction task comprehensively. In this paper, we present a novel insight along with a brand-new prediction framework by formulating multimodal prediction into three steps: modality clustering, classification and synthesis, and address the shortcomings of earlier frameworks. Exhaustive experiments on popular benchmarks have demonstrated that our proposed method surpasses state-of-the-art works even without introducing social and map information. Specifically, we achieve 19.2% and 20.8% improvement on ADE and FDE respectively on ETH/UCY dataset. Jianhua Sun 0003, Haoshu Fang, Cewu Lu |
ICCV | 3 |
| 2021 | Graspness Discovery in Clutters for Fast and Accurate Grasp DetectionabstractEfficient 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 uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of current grasp pose detection methods. In this paper, we propose "graspness", a quality based on geometry cues that distinguishes graspable area in cluttered scenes. A look-ahead searching method is proposed for measuring the graspness and statistical results justify the rationality of our method. To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process. Extensive experiments verify the stability, generality and effectiveness of our graspness model, allowing it to be used as a plug-and-play module for different methods. A large improvement in accuracy is witnessed for various previous methods after equipping our graspness model. Moreover, we develop GSNet, an end-to-end network that incorporate our graspness model for early filtering of low quality predictions. Experiments on a large scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30 + AP) and achieves a high inference speed. Chenxi Wang 0003, Haoshu Fang, Minghao Gou, Hongjie Fang, Cewu Lu |
ICCV | 2 |
| 2021 | RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD ImagesabstractGeneral object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most of the success grasps, or only take the unstable depth image or point cloud as input which may lead to poor results in some cases. In this paper, we propose RGBD-Grasp, a pipeline that solves this problem by decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth information are processed separately. In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image. Consequently, a Fast Analytic Searching(FAS) module calculates the opening width and the distance of the gripper to the grasp point. By decoupling the grasp detection problem and introducing the stable RGB modality, our pipeline alleviates the requirement for the high-quality depth image and is robust to depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion dataset compared with several baselines. Real robot experiments on a UR5 robot with an Intel Realsense camera and a Robotiq two-finger gripper show high success rates for both single object scenes and cluttered scenes. Our code and trained model are available at graspnet.net. Minghao Gou, Haoshu Fang, Zhanda Zhu, Chenxi Wang 0003, Cewu Lu |
ICRA | 2 |
| 2020 | GraspNet-1Billion: A Large-Scale Benchmark for General Object GraspingabstractObject grasping is critical for many applications, which is also a challenging computer vision problem. However, for cluttered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 97,280 RGB-D image with over one billion grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful by analytic computation, which is able to evaluate any kind of grasp poses without exhaustively labeling ground-truth. In addition, we propose an end-to-end grasp pose prediction network given point cloud inputs, where we learn approaching direction and operation parameters in a decoupled manner. A novel grasp affinity field is also designed to improve the grasping robustness. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments and our proposed network achieves the state-of-the-art performance. Our dataset, source code and models are publicly available at www.graspnet.net. Haoshu Fang, Chenxi Wang 0003, Minghao Gou, Cewu Lu |
CVPR | 1 |
| 2020 | PaStaNet: Toward Human Activity Knowledge EngineabstractExisting image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g., which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/. Yong-Lu Li 0001, Liang Xu 0012, Xinpeng Liu 0002, Xijie Huang, Haoshu Fang, Ze Ma, Cewu Lu |
CVPR | 7 |
| 2019 | CrowdPose: Efficient Crowded Scenes Pose Estimation and a New BenchmarkabstractMulti-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using the graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method. Hao Zhu 0008, Yihuan Mao, Haoshu Fang, Cewu Lu |
CVPR | 5 |
| 2019 | Transferable Interactiveness Knowledge for Human-Object Interaction DetectionabstractHuman-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates whether human and object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets, regardless of HOI category settings. Our core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference. On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We extensively evaluate the proposed method on HICO-DET and V-COCO datasets. Our framework outperforms state-of-the-art HOI detection results by a great margin, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network. Yong-Lu Li 0001, Xijie Huang, Liang Xu 0012, Ze Ma, Haoshu Fang, Cewu Lu |
CVPR | 6 |
| 2019 | Cross-Domain Adaptation for Animal Pose EstimationabstractIn this paper, we are interested in pose estimation of animals. Animals usually exhibit a wide range of variations on poses and there is no available animal pose dataset for training and testing. To address this problem, we build an animal pose dataset to facilitate training and evaluation. Considering the heavy labor needed to label dataset and it is impossible to label data for all concerned animal species, we, therefore, proposed a novel cross-domain adaptation method to transform the animal pose knowledge from labeled animal classes to unlabeled animal classes. We use the modest animal pose dataset to adapt learned knowledge to multiple animals species. Moreover, humans also share skeleton similarities with some animals (especially four-footed mammals). Therefore, the easily available human pose dataset, which is of a much larger scale than our labeled animal dataset, provides important prior knowledge to boost up the performance on animal pose estimation. Experiments show that our proposed method leverages these pieces of prior knowledge well and achieves convincing results on animal pose estimation. Jinkun Cao, Hongyang Tang, Haoshu Fang, Xiaoyong Shen, Yu-Wing Tai, Cewu Lu |
ICCV | 3 |
| 2019 | InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-PastingabstractInstance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and increase the diversity, previous methods have investigated using data annotation from other domain (e.g. bbox, point) in a weakly supervised mechanism. In this paper, we present a simple, efficient and effective method to augment the training set using the existing instance mask annotations. Exploiting the pixel redundancy of the background, we are able to improve the performance of Mask R-CNN for 1.7 mAP on COCO dataset and 3.3 mAP on Pascal VOC dataset by simply introducing random jittering to objects. Furthermore, we propose a location probability map based approach to explore the feasible locations that objects can be placed based on local appearance similarity. With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35.7 mAP to 37.9 mAP without modifying the backbone or network structure. Our method is simple to implement and does not increase the computational complexity. It can be integrated into the training pipeline of any instance segmentation model without affecting the training and inference efficiency. Our code and models have been released at https://github.com/GothicAi/InstaBoost. Haoshu Fang, Jianhua Sun 0003, Runzhong Wang, Minghao Gou, Yong-Lu Li 0001, Cewu Lu |
ICCV | 1 |
| 2018 | Learning Pose Grammar to Encode Human Body Configuration for 3D Pose EstimationabstractIn this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. Our model directly takes 2D pose as input and learns a generalized 2D-3D mapping function. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNN) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges. Haoshu Fang, Yuanlu Xu, Wenguan Wang, Xiaobai Liu, Song-Chun Zhu |
AAAI | 1 |
| 2018 | Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge TransferabstractHuman body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset [6], and we achieve state-of-the-art human parsing results. Our approach is general and can be readily extended to other object/animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code will be made publicly available. Haoshu Fang, Guansong Lu, Xiaolin Fang 0002, Jianwen Xie, Yu-Wing Tai, Cewu Lu |
CVPR | 1 |
| 2018 | Pairwise Body-Part Attention for Recognizing Human-Object Interactions
Haoshu Fang, Jinkun Cao, Yu-Wing Tai, Cewu Lu |
ECCV (10) | 1 |
| 2017 | RMPE: Regional Multi-person Pose EstimationabstractMulti-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve 76:7 mAP on the MPII (multi person) dataset[3]. Our model and source codes are made publicly available. Haoshu Fang, Shuqin Xie, Yu-Wing Tai, Cewu Lu |
ICCV | 1 |