Ryan Batke
Papers
3
Total Citations
38
H-Index
2
About
Ryan Batke is a robotics researcher whose work focuses on pushing the boundaries of dynamic bipedal locomotion through the innovative application of sim-to-real reinforcement learning. His primary research areas include legged robotics, optimal control, and the development of reduced-order models for agile maneuvers. Batke’s major contribution lies in bridging the gap between simulation and reality for highly dynamic behaviors, such as turning and other transient maneuvers, which are critical for robots to match the athletic capabilities of humans and animals. His most cited work, “Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning” (22 citations), introduces a method to generate reference trajectories for complex maneuvers using a single rigid-body model, enabling more robust sim-to-real transfer. This is complemented by his paper on “Dynamic Bipedal Turning through Sim-to-Real Reinforcement Learning” (14 citations), which demonstrates seamless transitions between gaits. With a total of 38 citations across his key papers, Batke’s research is laying the groundwork for more agile and versatile bipedal robots, making him a notable emerging voice in the field of dynamic locomotion.
Research Focus
Key Achievements
Top Papers
- 1
- 2Dynamic Bipedal Turning through Sim-to-Real Reinforcement Learning14 citations · 2022
- 3Dynamic Bipedal Maneuvers through Sim-to-Real Reinforcement Learning2 citations · 2022