About

Andrei Mitriakov is a robotics researcher specializing in the autonomous control of reconfigurable tracked robots, with a strong focus on reinforcement learning (RL) and simulation-to-reality transfer. His work addresses the critical challenge of enabling mobile robots to navigate complex, unstructured environments—particularly staircases—where traditional control methods fall short due to hardware diversity and environmental stochasticity. Mitriakov’s key contributions include developing RL-based frameworks that allow articulated tracked robots to learn staircase negotiation behaviors autonomously, and creating an open-source software framework (Gazebo/ROS-based) that serves as a reproducible research tool for the community. His most cited paper (2021, 20 citations) demonstrates successful simulation-to-reality transfer of staircase traversal skills. Beyond locomotion, Mitriakov has explored digital twin-driven smart home environments to support assistive robots for elderly and frail individuals. With a cumulative citation count approaching 60 across his top papers, his work is gaining traction for its practical, scalable approach to deploying intelligent robots in real-world settings—a promising step toward more capable and adaptable assistive robotics.

Research Focus

Key Achievements

5
H-Index
5
Papers
54
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
Reinforcement Learning Based, Staircase Negotiation Learning: Simulation and Transfer to Reality for Articulated Tracked Robots
20 citations · 2021
📈 Most Prolific Year: 2020 (2 Papers)
🤝 Key Collaborators: 6
🏛 Institutions: Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, Université de Bretagne Occidentale, Centre National de la Recherche Scientifique

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago