Published in Arxiv, 2025
Scaling up humanoid robot control via motion tracking
Recommended citation: Z. Luo, Y. Yuan, T. Wang, C. Li, S. Chen, F. Castaneda, Z. Cao, X. Da, etc. (2025). "SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control. " Arxiv. https://arxiv.org/abs/2511.07820
Published in Conference on Robot Learning, 2025
Scaling up robot datat collection with high quality human data
Recommended citation: S. Chen, Y. Ye, Z. Cao, J. Lew, P. Xu, C.K. Liu. (2025). "Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching. " CoRL. https://arxiv.org/abs/2508.03068
Published in Robotics: Science and Systems, 2025
Dexterous grasping and extrinsic manipulation under force uncertainty
Recommended citation: S. Chen, S. Marinovic, S. Iba, R. S. Zarrin. (2025). " GeoDEx: A Unified Geometric Framework for Tactile Dexterous and Extrinsic Manipulation under Force Uncertainty. " RSS. https://arxiv.org/abs/2505.00647
Published in IEEE International Conference on Robotics and Automation, 2025
Scaling up robot datat collection with high quality human data
Recommended citation: S. Chen, C. Wang, K, Nguyen, L, Fei-Fei, C.K Liu. (2025). "ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback. " ICRA. https://arxiv.org/abs/2410.08464
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (Oral), 2024
Learning extrinsic manipulation from single demonstration
Recommended citation: A. Wu, R. Wang, S. Chen, C. Eppner, C.K Liu. (2024). "One-Shot Transfer of Long-Horizon Extrinsic Manipulation Through Contact Retargeting. " IROS. https://arxiv.org/abs/2404.07468
Published in Robotics: Science and Systems, 2024
Dexterous precision grasp planning for shape uncertain objects
Recommended citation: S. Chen, J. Bohg, C.K Liu. (2024). "SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty. " RSS. https://arxiv.org/abs/2404.13532
Published in SIGGRAPH Conference proceedings, 2023
A framework for dexterous nonprehensile pregrasp manipulation for initially occluded ungraspable objects
Recommended citation: S. Chen, A. Wu, C.K Liu. (2023). "Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects. " SIGGRAPH. https://arxiv.org/abs/2305.04654
Published in Learning on Graph, 2022
Jointly predicting event type as well as event time on continuous dynamic graph of community.
Recommended citation: X. Wang,S. Chen, Y. He, M. Wang, Q. Gan, J. Yan, Y. Yang. (2022). "CEP3: Community Event Prediction with Neural Point Process on Graph. " LoG 2022. https://openreview.net/forum?id=sfc0rjCBqS_
Published in IEEE RA-L, 2022
Proposing a framework that can planning control steps in real time and adapt to parameter changes under observation and action noise.
Recommended citation: S. Chen, K. Werling, C.K Liu. (2022). "Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation. " IEEE-RAL. https://arxiv.org/abs/2202.09834
Published in IEEE RA-L, 2022
Using differentiable physics engine to learn low dimension state representation of deformable object.
Recommended citation: S. Chen,Y. Liu, S. Yao, J. Li, T. Fan, J. Pan. (2022). "DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator. " IEEE-RAL. https://arxiv.org/abs/2110.12352