Saad Abdul Ghani — accepted AI-conference papers

2026

Failing Gracefully: Mitigating Impact of Inevitable Robot Failures

Duc Nguyen, Saad Abdul Ghani, Andrew Marshall, Allison Andreyev, Gregory Stein, Xuesu Xiao

ICRA 2026poster

Service robots operate in household environments shared with humans, pets, and everyday objects, where they are highly susceptible to failures such as software crashes, hardware degradation, or unpredictable interactions. While roboticists strive to minimize failures, some remain inevitable, making …

Cited by 0 Scholar ↗

2025

Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination

Saad Abdul Ghani, Zizhao Wang, Peter Stone, Xuesu Xiao

IROS 2025

This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-b

Cited by 4 Source ↗Scholar ↗