Dr. Alireza Rastegarpanah is Assistant Professor in Robotics and AI at Aston University and Visiting Professor at the University of Brescia, Italy. He is also Co-Founder and Honorary Research Fellow at the Extreme Robotics Lab (ERL), University of Birmingham. In addition, he co-founded the Robotic Disassembly Testbed at the Birmingham Energy Innovation Centre and currently leads research on Robotic Disassembly for the Circular Economy at ERL, supervising a multi-tier team of PhD students, MSc researchers, and visiting interns. He received his PhD in Robotics from the University of Birmingham and has since built a strong international research portfolio across institutions such as University College London, the Faraday Institution, and the University of Birmingham. His core expertise lies in AI-driven robotics, including vision-guided manipulation, control systems, and robotic platforms for hazardous, medical, and industrial environments. His research integrates AI, machine vision, extended reality, and telerobotics to address real-world challenges in remanufacturing, healthcare, and sustainable automation.

Dr. Rastegarpanah has supervised multiple PhD students from inception to graduation. He has led or co-led many national and European research projects, including the €6M EU-funded project REBELION, and maintains long-standing collaborations with industry and RTOs. His funding record includes Horizon Europe, Royal Society, UKRI, NERC, and industry-funded research. He has published over 70 technical papers in high-impact journals and conferences, and serves as an editor and reviewer for several leading robotics and AI publications. In recognition of his national and international leadership in sustainable robotics, he received the Robotics Sustainability Leadership Award, jointly awarded by euRobotics and ARIA.

• AI-driven robotics for real-world applications
• Vision-guided robotic manipulation
• Human–robot interaction (HRI) and shared autonomy
• Telerobotics and human-in-the-loop control
• Robot learning, including reinforcement learning and learning from demonstration
• Machine vision and perception in unstructured environments
• Intelligent control systems and decision-making under uncertainty
• Multi-modal and multi-agent robotic systems
• Simulation-to-reality (sim2real) transfer and digital twins
• Extended reality (XR) for enhanced robotic control and operator training