Control Systems • Robotics • Deep Reinforcement Learning • Indoor Positioning
I work at the intersection of adaptive & nonlinear control, teleoperation, deep RL, and localization.
My research focuses on building robust, learning-based controllers for uncertain robotic systems and perception-driven navigation.
- Nonlinear & Adaptive Control
- Teleoperation & Human–Robot Interaction
- Elastic / Compliant Robot Control
- Deep Reinforcement Learning for Control (TD3, SAC)
- Indoor Positioning Systems (BLE / WiFi RSS / UWB)
- Sensor Fusion, EKF, SLAM
- BEV (Bird’s-Eye-View) perception & camera-based spatial understanding
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Deep Reinforcement Learning for Adaptive Gain Tuning in Teleoperation with Flexible Joints & Time-Varying Delays
Accepted, ICRoM 2025 — Hybrid P+d + TD3 controller. -
Bluetooth Low Energy for Indoor Positioning: Challenges, Algorithms and Datasets
Automation in Construction, IF 11.6 — Comprehensive BLE IPS survey. -
A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System
ICWR 2024 — 1D CNN for CIR-based NLoS/LoS classification. -
GA-Tuned Ensemble Learning for Improving the Performance of Wi-Fi RSS-Based Indoor Positioning
ICWR 2024 — GA-optimized ensemble for RSS-based localization. -
Enhancing Spatial Awareness: A Survey of Camera-Based Frontal View to Bird’s-Eye-View Conversion
Signal Processing: Image Communication — BEV perception survey. -
An Adaptive Controller for Cooperative Teleoperation System with Time-Varying Delay and Formation
ICRoM 2023 — Adaptive teleoperation under delay + formation constraints.
Email: Alighaemifar@gmail.com
Google Scholar: https://scholar.google.com/citations?hl=en&user=SgFJx0kAAAAJ
LinkedIn: https://www.linkedin.com/in/mohammadali-ghaemifar
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If you're working on RL for control, teleoperation, or navigation, feel free to connect.