32 citations · 36 across the 25 of their papers we have counts for
16 papers · 1 filter
Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data
Dianyi Yang, Mengya Liu, Zhumei Wang +9
Learning generalizable humanoid loco-manipulation remains bottlenecked by costly real-world demonstrations. Simulation provides a scalable alternative, but requires visually faithf…
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…
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Hong Li, Yue Xu, Yihan Tang +8
Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile…
EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
Yue Xu, Mingtao Nie, Tianle Li +4
Robot learning from real-world demonstrations is currently constrained by data scaling. Universal Manipulation Interface (UMI) provides an efficient robot-free data collection inte…
OmniClone: Engineering a Robust, All-Rounder Whole-Body Humanoid Teleoperation System
Yixuan Li, Le Ma, Yutang Lin +8
Whole-body humanoid teleoperation enables humans to remotely control humanoid robots, serving as both a real-time operational tool and a scalable engine for collecting demonstratio…
LessMimic: Long-Horizon Humanoid Interaction with Unified Distance Field Representations
Yutang Lin, Jieming Cui, Yixuan Li +3
Humanoid robots that autonomously interact with physical environments over extended horizons represent a central goal of embodied intelligence. Existing approaches rely on referenc…
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…
SafeFall: Learning Protective Control for Humanoid Robots
Ziyu Meng, Tengyu Liu, Le Ma +4
Bipedal locomotion makes humanoid robots inherently prone to falls, causing catastrophic damage to the expensive sensors, actuators, and structural components of full-scale robots.…
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…
CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
Yixuan Li, Yutang Lin, Jieming Cui +4
Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitat…
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…
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
Le Ma, Ziyu Meng, Tengyu Liu +4
Humanoid robots are anticipated to acquire a wide range of locomotion capabilities while ensuring natural movement across varying speeds and terrains. Existing methods encounter a…
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.,…
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.…