Puhao Li | 李浦豪

I am currently a Ph.D. student in Dept. of Automation, Tsinghua University advised by Prof. Song-Chun Zhu. I am also a research intern in General Vision Lab at Beijing Institute for General Artificial Intelligence (BIGAI), and I am grateful to be advised by Dr. Tengyu Liu and Dr. Siyuan Huang. Previously, I obtained my B.Eng. degree from Tsinghua University in 2023.

My research interests lie in the intersection of robotics manipulation and 3D computer vision. My long-term goal is to develop embodied intelligent systems capable of interpreting human intent and naturally interacting with people in various environments, learning reusable and endless low-level skill sets and high-level common sense. Currently, I am working on 3D scene understanding and robotic manipulation learning, pushing the boundaries of how robots operate within complex settings.

Email / CV / Google Scholar / Github / Twitter

Research

	PhyRecon: Physically Plausible Neural Scene Reconstruction Junfeng Ni, Yixin Chen, Bohan Jing, Nan Jiang, Bing Wang, Bo Dai, Puhao Li, Yixin Zhu, Song-Chun Zhu, Siyuan Huang arXiv 2024 [Paper] [Code] [Project Page] We introduce PhyRecon, which enables physically plausible 3D scene reconstruction. PhyRecon features a joint optimization framwork incorporating both differentiable rendering and physics-based objectives.
	Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations Puhao Li, Tengyu Liu, Yuyang Li, Muzhi Han, Haoran Geng, Shu Wang, Yixin Zhu, Song-Chun Zhu, Siyuan Huang IROS 2024 (Oral Pitch) [Paper] [Code] [Project Page] 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.
	Grasp Multiple Objects with One Hand Yuyang Li, Bo Liu, Yiran Geng, Puhao Li, Yaodong Yang, Yixin Zhu, Tengyu Liu, Siyuan Huang RA-L, presented at IROS 2024 (Oral Presentation) [Paper] [Code] [Data] [Project Page] We introduce MultiGrasp, a two-stage framework for simultaneous multi-object grasping with multi-finger dexterous hands. In addition, we contribute Grasp'Em, a large-scale synthetic multi-object grasping dataset.
	Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang, Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang CVPR 2024 (Highlight) [Paper] [Code] [Project Page] We introduce a novel two-stage framework that employs scene affordance as an intermediate representation, effectively linking 3D scene grounding and conditional motion generation.
	An Embodied Generalist Agent in 3D World Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, Siyuan Huang ICML 2024 ICLR 2024 @ LLMAgents Workshop [Paper] [Code] [Data] [Project Page] We introduce LEO, an embodied multi-modal and multi-task generalist agent that excels in perceiving, grounding, reasoning, planning, and acting in 3D world.
	Diffusion-based Generation, Optimization, and Planning in 3D Scenes Siyuan Huang, Zan Wang, Puhao Li, Baoxiong Jia, Tengyu Liu, Yixin Zhu, Wei Liang, Song-Chun Zhu CVPR 2023 [Paper] [Code] [Project Page] [Hugging Face] We introduce SceneDiffuser, a unified conditional generative model for 3D scene understanding. In contrast to prior work, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented.
	GenDexGrasp: Generalizable Dexterous Grasping Puhao Li, Tengyu Liu, Yuyang Li, Yiran Geng, Yixin Zhu, Yaodong Yang, Siyuan Huang ICRA 2023 [Paper] [Code] [Data] [Project Page] We introduce GenDexGrasp, a versatile dexterous grasping method that can generalize to out-of-domain robotic hands. In addition, we contribute MultiDex, a large-scale synthetic dexterous grasping dataset.
	DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation Ruicheng Wang, Jialiang Zhang, Jiayi Chen, Yinzhen Xu, Puhao Li, Tengyu Liu, He Wang ICRA 2023 (Oral Presentation, Outstanding Manipulation Paper Finalist) [Paper] [Code] [Data] [Project Page] We introduce a large-scale dexterous grasping dataset DexGraspNet, which based on simulation. DexGraspNet features more physical stability and higher diversity than previous grasping datasets.

Experience

	Tsinghua University, China 2023.09 - now Ph.D. Student Advisor: Prof. Song-Chun Zhu
	Beijing Institute for General Artificial Intelligence (BIGAI), China 2021.09 - now Research Intern Advisor: Dr. Tengyu Liu and Dr. Siyuan Huang
	Tsinghua University, China 2019.08 - 2023.06 Undergraduate Student

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