Max Robotics — Global Service Robot Directory + US Certification

About

Martin Servin’s research lies at the intersection of robotics, reinforcement learning, and heavy vehicle automation, with a particular focus on unstructured outdoor environments like forestry and mining. His major contributions center on developing and transferring deep reinforcement learning (DRL) controllers from simulation to real-world operation—a notoriously difficult challenge. In his most cited work, “Computational exploration of robotic rock loading” (18 citations), he laid foundational methods for autonomous material handling. His 2024 study on sim-to-real transfer of active suspension control for forestry vehicles (17 citations) is especially notable: it addresses the gap between lightweight robots and heavy, hydraulically-actuated machinery, demonstrating that DRL can effectively control active suspensions on rough terrain. Servin also advanced multi-log grasping using reinforcement learning and virtual visual servoing (2023, 5 citations), tackling the unstructured, harsh conditions of forest automation. His work is distinguished by its practical focus on heavy vehicles—such as forwarders and loaders—where safety, stability, and robustness are paramount. By bridging simulation and reality, Servin is helping to enable a new generation of autonomous forestry and mining equipment, with significant implications for safety, efficiency, and sustainability in these industries.