Muhammad Alhaddad — accepted AI-conference papers

2026

Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles

Aleksei Staroverov, Muhammad Alhaddad, Aditya Narendra, Konstantin Mironov, Aleksandr Panov

ICRA 2026poster

Generalist robot policies must operate safely and reliably in everyday human environments such as homes, offices, and warehouses, where people and objects move unpredictably. We present Dynamic Neural Potential Field (NPField-GPT), a learning-enhanced model predictive control (MPC) framework that co…

Cited by 0 code ↗Scholar ↗

2024

Neural Potential Field for Obstacle-Aware Local Motion Planning

Muhammad Alhaddad, Konstantin Mironov, Aleksey Staroverov, Aleksandr Panov

ICRA 2024poster

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network mode…

Cited by 5 Source ↗Scholar ↗