Haoran Geng | 耿浩然

I am a rising senior student in Turing Class at the School of Electronic Engineering and Computer Science(EECS), Peking University with GPA ranking 1/95. I'm also a research visitor at Stanford University and a research intern at Beijing Institute for General Artificial Intelligence (BIGAI). I am honored to be advised by Prof. He Wang, Prof. Leonidas J. Guibas and Dr. Siyuan Huang. In addition, I am privileged to work closely with Prof. Li Yi, Prof. Hao Dong, Prof. Yaodong Yang and Prof. Baoquan Chen. I am also grateful to have grown up and studied with my twin brother Yiran Geng, which has been a truly unique and special experience for me.

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@article{wan2023unidexgrasp++,
 title={UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning},
 author={Wan, Weikang and Geng, Haoran and Liu, Yun and Shan, Zikang and Yang, Yaodong and Yi, Li and Wang, He},
 journal={arXiv preprint arXiv:2304.00464},
 year={2023} }

We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting.

@article{gong2023arnold,
 title={ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes},
 author={Gong, Ran and Huang, Jiangyong and Zhao, Yizhou and Geng, Haoran and Gao, Xiaofeng and Wu, Qingyang and Ai, Wensi and Zhou, Ziheng and Terzopoulos, Demetri and Zhu, Song-Chun and Jia, Baoxiong and Huang, Siyuan},
 journal={arXiv preprint arXiv:2304.04321},
 year={2023}
}

We present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.

We introduce ReDMan, an open-source simulation platform that provides a standardized implementation of safe RL algorithms for Reliable Dexterous Manipulation.

@article{geng2022gapartnet,
 title={GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts},
 author={Geng, Haoran and Xu, Helin and Zhao, Chengyang and Xu, Chao and Yi, Li and Huang, Siyuan and Wang, He},
 journal={arXiv preprint arXiv:2211.05272},
 year={2022}
}

We propose to learn cross-category generalizable object perception and manipulation skills via Generalizable and Actionable Parts(GAPart), and present GAPartNet, a large-scale interactive dataset with rich part annotations.

@article{geng2023partmanip,
 title={PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations},
 author={Geng, Haoran and Li, Ziming and Geng, Yiran and Chen, Jiayi and Dong, Hao and Wang, He},
 journal={arXiv preprint arXiv:2303.16958},
 year={2023}
}

We introduce a large-scale, cross-category part-based object manipulation benchmark with tasks in realistic, vision-based settings and design a novel augmented state-to-vision distillation method for these challenging tasks.

@article{xu2023unidexgrasp,
 title={UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy},
 author={Xu, Yinzhen and Wan, Weikang and Zhang, Jialiang and Liu, Haoran and Shan, Zikang and Shen, Hao and Wang, Ruicheng and Geng, Haoran and Weng, Yijia and Chen, Jiayi and others},
 journal={arXiv preprint arXiv:2303.00938},
 year={2023}
}

We tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting.

@article{geng2022end,
 title={End-to-End Affordance Learning for Robotic Manipulation},
 author={Geng, Yiran and An, Boshi and Geng, Haoran and Chen, Yuanpei and Yang, Yaodong and Dong, Hao},
 journal={International Conference on Robotics and Automation (ICRA)},
 year={2023}
}

In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest.

I was selected for the Ministry of Education Talent Program and conducted physics research at Nankai University during my high school years.