collaborators

12 papers

cs.RO2026

AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation

Roohan Ahmed Khan, Yasheerah Yaqoot, Amir Atef Habel +2

Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks. However, its practical use still depends heavily on human…

cs.RO2026

GustPilot: A Hierarchical DRL-INDI Framework for Wind-Resilient Quadrotor Navigation

Amir Atef Habel, Roohan Ahmed Khan, Fawad Mehboob +2

Wind disturbances remain a key barrier to reliable autonomous navigation for lightweight quadrotors, where the rapidly varying airflow can destabilize both planning and tracking. T…

cs.RO2026

ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control

Faryal Batool, Yasheerah Yaqoot, Muhammad Ahsan Mustafa +3

Safe swarm navigation in cluttered indoor environment requires long-horizon planning, reactive obstacle avoidance, and adaptive compliance. We propose ImpedanceDiffusion, a hierarc…

cs.RO2026

Adaptive SINDy: Residual Force System Identification Based UAV Disturbance Rejection

Fawad Mehboob, Amir Atef Habel, Roohan Ahmed Khan +3

The stability and control of Unmanned Aerial Vehicles (UAVs) in a turbulent environment is a matter of great concern. Devising a robust control algorithm to reject disturbances is…

cs.RO2026

HumanDiffusion: A Vision-Based Diffusion Trajectory Planner with Human-Conditioned Goals for Search and Rescue UAV

Faryal Batool, Iana Zhura, Valerii Serpiva +4

Reliable human--robot collaboration in emergency scenarios requires autonomous systems that can detect humans, infer navigation goals, and operate safely in dynamic environments. T…

cs.RO2026

Glove2UAV: A Wearable IMU-Based Glove for Intuitive Control of UAV

Amir Habel, Ivan Snegirev, Elizaveta Semenyakina +6

This paper presents Glove2UAV, a wearable IMU-glove interface for intuitive UAV control through hand and finger gestures, augmented with vibrotactile warnings for exceeding predefi…