32 citations · 60 across the 16 of their papers we have counts for
17 papers
Towards Human-level Dexterous Teleoperation
Puhao Li, Zeyuan Chen, Yingying Wu +9
Humans routinely wield tools, swap grasps, and reposition objects within a single hand, seamlessly orchestrating contact transitions that span translation, reorientation, and finge…
Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation
Yuyang Li, Yinghan Chen, Zihang Zhao +4
Robotic manipulation requires both rich multimodal perception and effective learning frameworks to handle complex real-world tasks. See-through-skin (STS) sensors, which combine ta…
GWM: Towards Scalable Gaussian World Models for Robotic Manipulation
Guanxing Lu, Baoxiong Jia, Puhao Li +4
Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have show…
Ag2x2: Robust Agent-Agnostic Visual Representations for Zero-Shot Bimanual Manipulation
Ziyin Xiong, Yinghan Chen, Puhao Li +3
Bimanual manipulation, fundamental to human daily activities, remains a challenging task due to its inherent complexity of coordinated control. Recent advances have enabled zero-sh…
ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models
Puhao Li, Yingying Wu, Ziheng Xi +8
Learning real-world robotic manipulation is challenging, particularly when limited demonstrations are available. Existing methods for few-shot manipulation often rely on simulation…
MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans
Huangyue Yu, Baoxiong Jia, Yixin Chen +9
Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality stan…
Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation
Yuyang Li, Wenxin Du, Chang Yu +6
Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. As a promising solution, Vision-Based Tactile Sensors (VBTSs) offer high spatial re…
ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning
Kailin Li, Puhao Li, Tengyu Liu +2
Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, hu…
PhysPart: Physically Plausible Part Completion for Interactable Objects
Rundong Luo, Haoran Geng, Congyue Deng +5
Interactable objects are ubiquitous in our daily lives. Recent advances in 3D generative models make it possible to automate the modeling of these objects, benefiting a range of ap…
Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations
Puhao Li, Tengyu Liu, Yuyang Li +6
Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g.,…
PhyRecon: Physically Plausible Neural Scene Reconstruction
Junfeng Ni, Yixin Chen, Bohan Jing +7
We address the issue of physical implausibility in multi-view neural reconstruction. While implicit representations have gained popularity in multi-view 3D reconstruction, previous…
Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
Zan Wang, Yixin Chen, Baoxiong Jia +7
Despite significant advancements in text-to-motion synthesis, generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem pri…
An Embodied Generalist Agent in 3D World
Jiangyong Huang, Silong Yong, Xiaojian Ma +7
Leveraging massive knowledge from large language models (LLMs), recent machine learning models show notable successes in general-purpose task solving in diverse domains such as com…
Grasp Multiple Objects with One Hand
Yuyang Li, Bo Liu, Yiran Geng +5
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.…
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
Siyuan Huang, Zan Wang, Puhao Li +5
We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization…
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Ruicheng Wang, Jialiang Zhang, Jiayi Chen +4
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more unde…
GenDexGrasp: Generalizable Dexterous Grasping
Puhao Li, Tengyu Liu, Yuyang Li +4
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior a…