David Weiler
Papers
1
Total Citations
10
H-Index
1
About
David Weiler is a researcher at the intersection of robotics and reinforcement learning, with a primary focus on developing robust locomotion and manipulation skills for humanoid robots. His most cited work, "Deep Reinforcement Multi-Directional Kick-Learning of a Simulated Robot with Toes" (2021, 10 citations), provides a comprehensive analysis of using Proximal Policy Optimization (PPO) to train simulated NAO robots in the SimSpark environment. This study systematically investigates the influence of PPO hyperparameters, network architecture, and training setups on kick performance, demonstrating how simulated learning can transfer effectively to real-world game scenarios. Weiler's major contribution lies in bridging the gap between simulation and reality for complex, multi-directional motor skills, particularly by incorporating toe articulation—a subtle but critical detail for stable and powerful kicks. His work has direct implications for RoboCup competitions and broader humanoid robotics, offering a reproducible framework for skill acquisition. With a growing citation footprint, Weiler is establishing himself as a key contributor to deep reinforcement learning for physically grounded agents, advancing the state of the art in autonomous robot behavior.
Research Focus
Key Achievements
Top Papers
- 1