Zachary Olkin
Zachary Olkin is a PhD student in Control and Dynamical Systems at the California Institute of Technology and an NSF Graduate Research Fellow. He conducts robotics research in the Amber Lab at Caltech, focusing on humanoid locomotion through reinforcement learning guided by model-based control methods. His published work addresses CLF-guided reinforcement learning for humanoid running, bilevel optimization for real-time gait generation, and perceptive multi-skill locomotion on hardware platforms such as the Unitree G1 robot.
“Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators”
Source→“His prior work on 'Chasing Autonomy' (reference [4]) established the CLF-RL running framework that this paper builds upon”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.