Frederik Ebert
CEO at Emancro
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolFrederik Ebert is a robotics-focused entrepreneur and CEO of Emancro, building mobile robotic manipulators to automate hospital logistics based on his PhD research at UC Berkeley. With 11 years of experience spanning academic research, industry internships, and hands-on mechatronics engineering in Europe, he blends model-based deep reinforcement learning with practical robot design and actuation. His PhD under Sergey Levine and Chelsea Finn produced algorithms for robotic manipulation that he has moved toward real-world deployment, and his contributions to Berkeley’s deep RL coursework show hands-on implementation of policy-gradient methods. Comfortable across low-level actuator design to high-level learning systems, Frederik uniquely combines hydraulics, series-elastic actuation, and modern deep RL to solve perceptual and control challenges in healthcare automation. Based in Berkeley, he brings a rare mix of academic rigor and product-driven execution to robotics startups.
11 years of coding experience
11 years of employment as a software developer
University of California, BerkeleyMechatronics Robotics and Automation Engineering, Mechatronics Robotics and Automation Engineering at Tongji UniversityRobotics Cognition Intelligence, Robotics Cognition Intelligence at Technical University of Munich
German, English, Chinese
Stackoverflow
13reputation
713reached
0answers
1question
Github Skills (10)
deep-learning10
tensorflow10
python10
reinforcement-learning10
numpy9
mlp9
openai-gym7
gymnasium7
machine-learning7
theano6
Programming languages (4)
Github contributions (5)
berkeleydeeprlcourse/homework_fall2019
Sep 2019 - Nov 2019
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 8 pushes, 1 branch in 2 months
Contributions summary:Frederik made multiple commits that focused on implementing and releasing homework assignments related to policy gradient methods within a deep reinforcement learning course. They added code, particularly in `pg_agent.py`, that involved calculating Q-values and advantages. Additionally, the user made changes to the `rl_trainer.py` and `train_ac_exploration_f18.py` files, indicating involvement in training and exploration strategies within the context of reinforcement learning.
febert/dynamic_multiview_3d
Oct 2017 - Dec 2017
Contributions:42 commits, 19 pushes, 1 branch in 2 months
pytorchsequencedeep-learningsingle-imagecomputer-vision
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