Researcher, Locomotion

The role

You will design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on the Asimov humanoid robot. You will own the sim2real pipeline and work to make motion robust on real hardware.

What you will do

• Design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on Asimov
• Own the sim2real pipeline end to end
• Push balance, gait, and recovery behavior into real-world environments
• Build and refine reward design, domain randomization, and training environments in MuJoCo
• Close the loop between simulation and hardware with real telemetry
• Work with hardware, controls, and manipulation researchers
• Open-source work and write up learnings

Required qualifications

• Deep hands-on experience with reinforcement learning for continuous control, legged locomotion, or whole-body control
• A track record of getting learned policies onto real robots
• Strong command of a physics simulator such as MuJoCo, Isaac, or similar, including reward shaping and domain randomization
• Fluency in Python and modern RL tooling
• Comfort in a ROS2-based control stack
• A bias for shipping
• Clear thinking about why a policy fails

Nice to have

• Published work in locomotion, legged robotics, or sim2real transfer
• Experience with model predictive control or classical locomotion methods alongside learning-based approaches
• Contributions to open-source robotics or RL projects
• Experience bringing up new hardware and debugging the messy gap between a model and a motor

About the company

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it.