Shengcheng Luo | 罗盛成

I am an incoming Ph.D. candidate at the School of Information Science and Technology, ShanghaiTech University, and the Beijing Institute for General Artificial Intelligence (BIGAI). I am fortunate to be co-supervised by Prof. Chenxi Xiao and Prof. Ziyuan Jiao.

I hold a B.Sc. from ShanghaiTech and a M.Sc. from University of Aizu under Prof. Yong Liu’s supervision, and previously spent two years at Shanghai Jiao Tong University’s MVIG Group working with Prof. Cewu Lu and Prof.Yonglu Li.

My research focuses on dexterous manipulation, tactile sensing, and end-effector design. I’m particularly interested in the intersection of robotics and AI, and always open to discussing potential collaborations or research exchanges.

I welcome collaborations with undergraduate researchers and industry partners passionate about dexterous manipulation and haptic systems, particularly those interested in transforming lab research into real-world applications through prototype development or industrial problem-solving - feel free to reach me!

news

Apr 24, 2025	Our paper “Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition” has been selected as award finalist by ICRA 2025. Thanks to Peng Quanquan and Lv Jun!
Jan 28, 2025	Our paper “Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition” has been accepted by ICRA 2025.
Jul 01, 2024	Our paper “DISCO: Embodied Navigation and Interaction via Differentiable Scene Semantics and Dual-Level Control” has been accepted by ECCV 2024.

selected publications

ICRA 2025
Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition

Shengcheng Luo^*, Quanquan Peng^*, Jun Lv^*, Kaiwen Hong, Katherine Rose Driggs-Campbell, Cewu Lu, and Yong-Lu Li

In International Conference on Robotics and Automation, 2025

Best Paper Finalist Abs Bib PROJECT Video

We have been selected as the best paper finalist for our paper in the International Conference on Robotics and Automation (ICRA) 2025 and the best paper ofHuman robot interaction track.

Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system poses significant challenges due to its high dimensionality, complex motions, and differences in physiological structure.In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, facilitating simultaneous human demonstration collection and robot manipulation teaching. In this setup, as data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control.We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
@inproceedings{luo2024humanagentjointlearningefficient, title = {Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition}, author = {Luo, Shengcheng and Peng, Quanquan and Lv, Jun and Hong, Kaiwen and Driggs-Campbell, Katherine Rose and Lu, Cewu and Li, Yong-Lu}, year = {2025}, eprint = {2407.00299}, booktitle = {International Conference on Robotics and Automation}, archiveprefix = {arXiv}, primaryclass = {cs.RO}, url = {https://arxiv.org/abs/2407.00299}, }

ECCV 2024

DISCO: Embodied Navigation and Interaction via Differentiable Scene Semantics and Dual-Level Control

Xinyu Xu, Shengcheng Luo, Yanchao Yang, Yong-Lu Li, and Cewu Lu

In European Conference on Computer Vision, 2024

Bib

@inproceedings{xu2024disco,
  title = {DISCO: Embodied Navigation and Interaction via Differentiable Scene Semantics and Dual-Level Control},
  author = {Xu, Xinyu and Luo, Shengcheng and Yang, Yanchao and Li, Yong-Lu and Lu, Cewu},
  booktitle = {European Conference on Computer Vision},
  pages = {108--125},
  year = {2024},
  organization = {Springer},
}

ACM MM 2021

Syntropic Counterpoints: Metaphysics of The Machines

Predrag K Nikolic, Ruiyang Liu, and Shengcheng Luo

In Proceedings of the 29th ACM International Conference on Multimedia, 2021

Bib

@inproceedings{nikolic2021syntropic,
  title = {Syntropic Counterpoints: Metaphysics of The Machines},
  author = {Nikolic, Predrag K and Liu, Ruiyang and Luo, Shengcheng},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  pages = {1443--1445},
  year = {2021},
}