6 citations · 12 across the 16 of their papers we have counts for
17 papers
HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing
Zhenjie Yang, Xingyu Jiao, Guopeng Zhong +18
Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abu…
UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
Lirui Zhao, Modi Shi, Li Chen +3
Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the qu…
MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation
Zhuoheng Li, Qingquan Lin, Checheng Yu +5
High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand space, increase end-effector…
TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks
Longyan Wu, Jieji Ren, Chenghang Jiang +6
Handheld paradigms offer an efficient and intuitive way for collecting large-scale demonstration of robot manipulation. However, achieving contact-rich bimanual manipulation throug…
: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies
Checheng Yu, Chonghao Sima, Gangcheng Jiang +14
High-reliability long-horizon robotic manipulation has traditionally relied on large-scale data and compute to understand complex real-world dynamics. However, we identify that the…
Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation
Hai Zhang, Siqi Liang, Li Chen +5
Why must vision-language navigation be bound to detailed and verbose language instructions? While such details ease decision-making, they fundamentally contradict the goal for navi…
EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration
Modi Shi, Shijia Peng, Jin Chen +6
Human demonstrations offer rich environmental diversity and scale naturally, making them an appealing alternative to robot teleoperation. While this paradigm has advanced robot-arm…
WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control
Haoran Jiang, Jin Chen, Qingwen Bu +8
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient…
Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data
Yixuan Pan, Ruoyi Qiao, Li Chen +8
Humanoid robots are envisioned to perform a wide range of tasks in human-centered environments, requiring controllers that combine agility with robust balance. Recent advances in l…
Is Diversity All You Need for Scalable Robotic Manipulation?
Modi Shi, Li Chen, Jin Chen +7
Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robo…
FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation
Longyan Wu, Checheng Yu, Jieji Ren +5
Enabling robots with contact-rich manipulation remains a pivotal challenge in robot learning, which is substantially hindered by the data collection gap, including its inefficiency…
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
Qingwen Bu, Yanting Yang, Jisong Cai +5
A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabili…
Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation
Qingwen Bu, Jia Zeng, Li Chen +7
Despite significant progress in robotics and embodied AI in recent years, deploying robots for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an ope…
Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation
Qingwen Bu, Hongyang Li, Li Chen +5
The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cro…
Learning Manipulation by Predicting Interaction
Jia Zeng, Qingwen Bu, Bangjun Wang +12
Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…
Embodied Understanding of Driving Scenarios
Yunsong Zhou, Linyan Huang, Qingwen Bu +7
Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded…
Fully Sparse 3D Occupancy Prediction
Haisong Liu, Yang Chen, Haiguang Wang +6
Occupancy prediction plays a pivotal role in autonomous driving. Previous methods typically construct dense 3D volumes, neglecting the inherent sparsity of the scene and suffering…