Shengcheng Luo

I’m a research assistant in the Computer Sciences Department at the Shanghai Jiao Tong University. I am advised by Dr. Yong-lu Li, and am a member of the MVIG-RHOS Group. I completed my B.Sc. at ShanghaiTech University and M.Sc. at University of Aizu, where I was advised by Dr. Yong Liu. My academic interests are embodied intelligence and robotics.

Feel free to reach out luoshch[at]shanghaitech[dot]edu[dot]cn. I’m happy to chat and connect!

news

Jul 28, 2024	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.
Nov 07, 2015	A long announcement with details

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

Abs Bib PROJECT Video

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},
}