Locomotion Researcher for Humanoid Robots

Menlo; Singapore; On-site; SGD 120,000 - 170,000; Full time

Job summary

Menlo Research is seeking a Researcher, Locomotion to push Asimov from walking to running, recovering, and moving through the real world with the confidence of something alive. You will own policies that keep motion robust on real hardware and extend sim2real from simulation to physical robots.

You will collaborate with hardware, controls, and manipulation researchers, design reward schemes and training environments, and publish learnings to advance open-source robotics.

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, not only into papers or simulators.
• 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, and comfort in a ROS2-based control stack.
• A bias for shipping: prefer real hardware validation over purely simulated results.
• Clear thinking about why a policy fails, not just whether it does.

Responsibilities

• Design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on Asimov.
• Own the sim2real pipeline end to end, building on zero-shot sim2real work.
• Push balance, gait, and recovery behavior beyond demos to survive real-world disturbances.
• Build and refine reward design, domain randomization, and training environments in MuJoCo.
• Close the loop between simulation and hardware with real telemetry from robots and feed insights back to the policy.
• Collaborate with hardware, controls, and manipulation researchers across teams.
• Open-source what you can and share learnings with the community.

Skills

Reinforcement learning

Legged locomotion

Whole-body control

Python

ROS2

MuJoCo

Research

Tools

MuJoCo

Isaac

ROS2

Job description

Menlo Research is seeking a Researcher, Locomotion to push Asimov from walking to running, recovering, and moving through the real world with the confidence of something alive. You will own policies that keep motion robust on real hardware and extend sim2real from simulation to physical robots.

You will collaborate with hardware, controls, and manipulation researchers, design reward schemes and training environments, and publish learnings to advance open-source robotics.