Alan Fern
Papers
20
Total Citations
423
H-Index
11
About
Alan Fern is a prominent robotics and machine learning researcher whose work centers on bipedal locomotion, reinforcement learning, and sim-to-real transfer for legged robots. He is perhaps best known for his sustained contributions to advancing the capabilities of the bipedal robot Cassie, where his research has pushed the boundaries of what autonomous robots can physically achieve. His 2018 paper on fast online trajectory optimization for Cassie (135 citations) laid critical groundwork for real-time multi-step motion planning, simultaneously optimizing center of mass motion and footholds. Building on this foundation, Fern and his collaborators have pioneered sim-to-real reinforcement learning approaches that enable robots to handle dynamic loads, challenging terrain, and vision-guided navigation — challenges that remain at the frontier of the field. His more recent work extends into humanoid loco-manipulation and high-speed running gaits, including a compelling comparison of optimized robot locomotion against human sprinting. With rigorous reward design evaluation and stepping-stone locomotion research further rounding out his portfolio, Fern has established himself as a leading voice shaping how intelligent, agile bipedal robots learn to move robustly in the real world.
Research Focus
Key Achievements
Top Papers
- 1Fast Online Trajectory Optimization for the Bipedal Robot Cassie135 citations · 2018
- 2Learning Task Space Actions for Bipedal Locomotion45 citations · 2021
- 3Learning Vision-Based Bipedal Locomotion for Challenging Terrain32 citations · 2024
- 4
- 5Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads29 citations · 2022
- 6Sim-to-Real Learning for Humanoid Box Loco-Manipulation25 citations · 2024
- 7
- 8
- 9Monte-Carlo Planning for Agile Legged Locomotion18 citations · 2018
- 10Learning Dynamic Bipedal Walking Across Stepping Stones15 citations · 2022