Papers
2
Total Citations
153
H-Index
2
About
Charles Game is a leading researcher at the intersection of robotics and artificial intelligence, with a primary focus on using deep reinforcement learning to enable complex, agile locomotion and manipulation in humanoid robots. His most influential work, “Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning” (2024), has garnered 147 citations and demonstrates a groundbreaking achievement: training a low-cost, 20-actuator miniature humanoid to play simplified one-versus-one soccer. This research proved that deep RL can synthesize sophisticated, safe movement skills that compose into dynamic, real-time behavioral strategies, pushing the boundaries of what is possible with affordable hardware. Game’s contributions are pivotal for advancing autonomous robots capable of operating in unpredictable, human-centric environments. His work not only showcases the practical power of deep RL but also opens new avenues for research in robot sports and dexterous control, making him a key figure in the ongoing effort to create truly agile and intelligent machines.
Research Focus
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Top Papers
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