During my three month internship, I ran a total of 89 experiments (most with multiple iterations over some selection of hyperparameters), had 261 commits across 7 branches of the project codebase, and gained valuable experience on the know-how for an interdisciplinary machine learning research project in industry.
Working in the software team of Project SARAM, I:
- delved deep into RL literature and theory (self-studied all of this specialization) and state of the art from OpenAI (etc. etc. etc.) which we based our work off on
- fine-tuned existing models, reducing average episode length for the task by ~25% while investigating potential approaches for sim2real transfer
- incorporated the newest URDF robot model and object meshes for the env
- developed the new simulation env, adding robust env dynamics/agent configurations and maintaining good code modularity
- proved that the robot is able to learn solely via exploration, prioritize and plan its own trajectories while producing human-like stirring techniques
Here’s an abridged version of the presentation deck (only demo clips) I gave at the intern showcase to over a hundred people including Samsung Research America’s President, Joon Lee.
Michael Sun 