Search Results for author: Puhao Li

Found 6 papers, 5 papers with code

Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance

1 code implementation26 Mar 2024 Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang, Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang

Despite significant advancements in text-to-motion synthesis, generating language-guided human motion within 3D environments poses substantial challenges.

Motion Synthesis

An Embodied Generalist Agent in 3D World

1 code implementation18 Nov 2023 Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, Siyuan Huang

Leveraging massive knowledge and learning schemes from large language models (LLMs), recent machine learning models show notable successes in building generalist agents that exhibit the capability of general-purpose task solving in diverse domains, including natural language processing, computer vision, and robotics.

3D dense captioning Question Answering +3

Grasp Multiple Objects with One Hand

1 code implementation24 Oct 2023 Yuyang Li, Bo Liu, Yiran Geng, Puhao Li, Yaodong Yang, Yixin Zhu, Tengyu Liu, Siyuan Huang

The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation.

Object

DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation

no code implementations6 Oct 2022 Ruicheng Wang, Jialiang Zhang, Jiayi Chen, Yinzhen Xu, Puhao Li, Tengyu Liu, He Wang

Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology.

Object

GenDexGrasp: Generalizable Dexterous Grasping

1 code implementation3 Oct 2022 Puhao Li, Tengyu Liu, Yuyang Li, Yiran Geng, Yixin Zhu, Yaodong Yang, Siyuan Huang

By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.

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