Haoran Geng | 耿浩然

I am an incoming PhD student at UC Berkeley. Previously, I was a visiting scholar at Stanford University through UGVR program. I received my Bachelor's degree with honors from Turing Class, Peking University. I am honored to be advised by Prof. Leonidas J. Guibas, Prof. He Wang, and Dr.Siyuan Huang. In addition, I am privileged to work closely with Prof. Li Yi, Prof. Hao Dong, Prof. Yaodong Yang and Prof. Yue Wang. 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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@misc{kuang2024ramretrievalbasedaffordancetransfer,
title={RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation},
author={Yuxuan Kuang and Junjie Ye and Haoran Geng and Jiageng Mao and Congyue Deng and Leonidas Guibas and He Wang and Yue Wang}, year={2024},
eprint={2407.04689},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2407.04689},
}

RAM proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, featuring generalizability across various objects, environments, and embodiments.

We present Open6DOR, a challenging and comprehensive benchmark for open-instruction 6-DoF object rearrangement tasks. Following this, we propose a zero-shot and robust method, Open6DORGPT, which proves effective in demanding simulation environments and real-world scenarios.

Simulately is a project where we gather useful information of robotics & physics simulators for cutting-edge robot learning research.

@misc{geng2023sage,
title={SAGE: Bridging Semantic and Actionable Parts for GEneralizable Articulated-Object Manipulation under Language Instructions},
author={Haoran Geng and Songlin Wei and Congyue Deng and Bokui Shen and He Wang and Leonidas Guibas},
year={2023},
eprint={2312.01307},
archivePrefix={arXiv},
primaryClass={cs.RO} }

We present SAGE, a framework bridging the understanding of semantic and actionable parts for generalizable manipulation of articulated objects using Large Language Models(LLMs) and Visual-Language Models(VLMs).

We introduce Ag2Manip, which enables various robotic manipulation tasks without any domain-specific demonstrations. Ag2Manip also supports robust imitation learning of manipulation skills in the real world.

@article{shapellm24,
author = {Zekun Qi and Runpei Dong and Shaochen Zhang and Haoran Geng and Chunrui Han and Zheng Ge and He Wang and Li Yi and Kaisheng Ma},
title = {ShapeLLM: Universal 3D Object Understanding for Embodied Interaction},
journal = {CoRR},
volume = {abs/2402.17766},
year = {2024},
eprinttype = {arXiv},
eprint = {2402.17766},
}

We present ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.

@misc{li2023manipllm,
title={ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation},
author={Xiaoqi Li and Mingxu Zhang and Yiran Geng and Haoran Geng and Yuxing Long and Yan Shen and Renrui Zhang and Jiaming Liu and Hao Dong},
year={2023},
eprint={2312.16217},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

We present ManipLLM, introducing an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation.

@misc{
you2023make,
title={Make a Donut: Language-Guided Hierarchical EMD-Space Planning for Zero-shot Deformable Object Manipulation},
author={Yang You and Bokui Shen and Congyue Deng and Haoran Geng and He Wang and Leonidas Guibas},
year={2023},
eprint={2311.02787},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

In this work, we introduce a demonstration-free hierarchical planning approach capable of tackling intricate long-horizon tasks without necessitating any training

@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.

@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.