Viktor Wiberg
Papers
3
Total Citations
25
H-Index
3
About
Viktor Wiberg is a researcher at the forefront of applying deep reinforcement learning (DRL) to heavy off-road vehicles, with a focus on forestry automation and rough-terrain mobility. His major contributions lie in bridging the “sim-to-real” gap for active suspension control, demonstrating that DRL policies trained in simulation can be effectively transferred to full-scale forestry vehicles—a domain traditionally dominated by lightweight robots. His most cited work (2024, 17 citations) validates this approach on a heavy vehicle navigating rugged terrain, marking a significant step toward practical, autonomous forest machinery. Wiberg also tackles the complex challenge of multi-log grasping, using reinforcement learning and virtual visual servoing to enable automated forwarding in unstructured environments (2023, 5 citations). By addressing the harsh, variable conditions of forestry, his research pushes the boundaries of robot control beyond controlled labs into real-world industrial applications. With a growing citation impact, Wiberg’s work is essential reading for anyone interested in sim-to-real transfer, heavy vehicle autonomy, or the future of sustainable forestry through intelligent robotics.
Research Focus
Key Achievements
Top Papers
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