3 citations · 4 across the 9 of their papers we have counts for
9 papers
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Yen-Jen Wang, Jiaman Li, Sirui Chen +9
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egoc…
SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction
Sirui Chen, Shibo Zhao, Zhen Wu +3
Current humanoid reinforcement-learning policies excel at free-space motions but struggle with contact-rich tasks, as pure kinematic tracking cannot resolve the physical ambiguitie…
Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
Ji Woong Kim, Ke Wang, Zipeng Fu +4
Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward i…
CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation
Sirui Chen, Zi-ang Cao, Zhengyi Luo +7
Recent progress in humanoid robots has unlocked agile locomotion skills, including backflipping, running, and crawling. Yet it remains challenging for a humanoid robot to perform f…
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Zhengyi Luo, Ye Yuan, Tingwu Wang +25
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid contro…
Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching
Sirui Chen, Yufei Ye, Zi-Ang Cao +3
We propose Hand-Eye Autonomous Delivery (HEAD), a framework that learns navigation, locomotion, and reaching skills for humanoids, directly from human motion and vision perception…
GeoDEx: A Unified Geometric Framework for Tactile Dexterous and Extrinsic Manipulation under Force Uncertainty
Sirui Chen, Sergio Aguilera Marinovic, Soshi Iba +1
Sense of touch that allows robots to detect contact and measure interaction forces enables them to perform challenging tasks such as grasping fragile objects or using tools. Tactil…
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
Sirui Chen, Chen Wang, Kaden Nguyen +2
Recent progress in imitation learning from human demonstrations has shown promising results in teaching robots manipulation skills. To further scale up training datasets, recent wo…
SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty
Sirui Chen, Jeannette Bohg, C. Karen Liu
Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studi…