Martin Spitznagel
Papers
1
Total Citations
10
H-Index
1
About
Martin Spitznagel is a researcher in robotics and artificial intelligence, with a primary focus on deep reinforcement learning for humanoid locomotion and manipulation. His most-cited work, "Deep Reinforcement Multi-Directional Kick-Learning of a Simulated Robot with Toes" (2021), provides a comprehensive analysis of using Proximal Policy Optimization (PPO) to train simulated NAO robots in the SimSpark environment. Spitznagel’s major contribution lies in systematically investigating how PPO hyperparameters, network architecture, and training setups influence the learning of complex, multi-directional kick behaviors—a challenging task for humanoid robots. Notably, his work also evaluates performance in real-game scenarios, bridging simulation-to-reality gaps. With 10 citations, this study has informed subsequent research in robot skill acquisition and reinforcement learning. Spitznagel’s achievements include advancing the understanding of toe joint utilization in robotic kicking, a subtle but impactful design choice that enhances motion efficiency. His research is valuable for students and engineers working on autonomous robot control, offering practical insights into training robust, adaptable behaviors for competitive robotics platforms.
Research Focus
Key Achievements
Top Papers
- 1