ML, Reinforcement Learning

You will own reinforcement learning across our stack, from locomotion policies to RL applied on top of learned behaviors. The research problems here are open ones: whole-body control under contact, sim-to-real transfer that holds up in environments we have never seen, and learning from a growing body of real-world robot experience.

This is a role for someone who wants their research to run on hardware in the field rather than on a benchmark, and who is early enough in a team's life to set its technical direction.

• Own RL across our stack, from locomotion to learned behaviors, and set the technical direction for how we apply it.
• Track where the field is going, and make calls about which directions are worth betting on and which are noise.
• Advance the state of the art in RL for embodied systems, and ship the results on real robots.
• Apply RL to improve learned behaviors beyond what demonstrations alone can reach, including fine-tuning imitation-learned and pretrained policies.