M.-Mahdi Naddaf-Sh — accepted AI-conference papers

2025

Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation

Ian T. Chuang, Andrew Lee, Dechen Gao, M.-Mahdi Naddaf-Sh, Iman Soltani

ICRA 2025

Imitation learning has demonstrated significant potential in performing high-precision manipulation tasks using visual feedback. However, it is common practice in imitation learning for cameras to be fixed in place, resulting in issues like occlusion and limited field of view. Furthermore, cameras a

Cited by 33 Source ↗code ↗Scholar ↗

2024

Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller

Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh, Devesh Upadhyay, Sicun Gao, Chuchu Fan

ICRA 2024poster

Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficult to use in real time. To address this issue, we propose a new generalizable control barrier function (CBF)based steeri…

Cited by 7 Source ↗code ↗Scholar ↗