Hongjie (Tony) Fang 方泓杰

I am a second-year Ph.D. student @ Computer Science in Shanghai Jiao Tong University (SJTU) & Shanghai Artificial Intelligence Laboratory, advised by Prof. Cewu Lu. Previously, I got my B. Eng. degree @ Computer Science and Engineering, and B. Ec. degree @ Finance from SJTU in 2022.
The pronounciation of my Chinese name is [hɒndʒɪə fɑ:n].

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[Jan. 2024] Four papers (AirExo, RH20T, Open X-Embodiment and AnyGrasp) are accepted by ICRA 2024. See you in Japan!
[Nov. 2023] AirExo is accepted by CoRL 2023 @ TGR as an oral presentation. I will give a talk about AirExo in the workshop.
[Oct. 2023] Open X-Embodiment is released! Proud of this wonderful collaboration with so many great robotic researchers!
[Sept. 2023] AirExo is released! Check our website for more details.
[Jun. 2023] One paper is accepted by IROS 2023.
[Jun. 2023] Our paper RH20T is accepted by RSS 2023 Learning for Task and Motion Planning Workshop.
[Apr. 2023] Our paper AnyGrasp is accepted by T-RO.
[Feb, 2023] Our paper "Target-referenced Reactive Grasping for Dynamic Objects" is accepted by CVPR 2023.
[Feb, 2023] Our paper TransCG is accepted by ICRA 2023 as RA-L submission. See you in London!
[Dec, 2022] The preprint version of our paper AnyGrasp is released. For more details, see AnyGrasp official page.
[Jun, 2022] Our paper TransCG is accepted by RA-L. For more details, see TransCG official page
[Aug, 2021] Our paper Graspness is accepted by ICCV 2021.

My research interests mainly lie on robotic fields, specifically, robotic manipulation and grasping. I am also interested in computer vision. I am currently a member of GraspNet in SJTU Machine Vision and Intelligence Group (SJTU-MVIG). Representative papers are highlighted. * denotes equal contribution.

Introduce the Open X-Embodiment Dataset, the largest robot learning dataset to date with 1M+ real robot trajectories, spanning 22 robot embodiments. Train large, transformer-based policies on the dataset (RT-1-X, RT-2-X) and show that co-training with our diverse dataset substantially improves performance.

Develop AirExo, a low-cost, adaptable, and portable dual-arm exoskeleton, for joint-level teleoperation and demonstration collection. Further leverage AirExo for learning with cheap demonstrations in the wild to improve sample efficiency and robustness of the policy.

Collect a dataset comprising over 110k 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, along with a corresponding human demonstration video. Put significant efforts in calibrating all the sensors and ensures a high-quality dataset.

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 trajectories. Designs a future grasp prediction algorithm to enhance the system's adaptability to dynamic handover scenes.

Focus on semantic consistency instead of temporal smoothness of the predicted grasp poses during reactive grasping. Solve the reactive grasping problem in a target-referenced setting by tracking through generated grasp spaces.

Propose a powerful AnyGrasp model for general grasping, including static scenes and dynamic scenes. AnyGrasp can generate accurate, full-DoF, dense and temporally-smooth grasp poses efficiently, and it works robustly against large depth sensing noise.

Propose TransCG, a large-scale real-world dataset for transparent object depth completion, along with a depth completion method DFNet based on the TransCG dataset.

Propose graspness, a quality based on geometry cues that distinguishes graspable area in cluttered scenes, which can be measured by a look-ahead searching method. Propose a graspness model to approximate the graspness value for quickly detect grasps in practice.

Provides an easy and unified interface for robots, grippers, sensors and pedals.

Proposes that we can learn "jargons" like "ResNet" and "YOLO" from academic paper citation information, and such citation information can be regarded as the searching results of the corresponding "jargon". For example, when searching "ResNet", the engine should return the "Deep Residual Learning for Image Recognition", instead of the papers that contains word "ResNet" in their titles, as current scholar search engines commonly return.

Reviewer: ICRA 2023/2024, IROS 2023.

Some of My Notes ---> Notes

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