Shengcheng Luo — accepted AI-conference papers

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 +1

ICRA 2025

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 presents inherent challenges due to the task's high dimension

Cited by 18 Source ↗code ↗Scholar ↗

2024

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

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

ECCV 2024poster

"Building a general-purpose intelligent home-assistant agent skilled in diverse tasks by human commands is a long-term blueprint of embodied AI research, which poses requirements on task planning, environment modeling, and object interaction. In this work, we study primitive mobile manipulations for…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗