RL Locomotion Researcher for Humanoid Robots

Ngpartners; Palo Alto (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Ngpartners is seeking a Researcher, Locomotion to advance Asimov from walking to running, learning to recover and maneuver in the real world. You will own policies for robust whole-body locomotion on hardware, not just in simulation.

You will collaborate with hardware, controls, and manipulation researchers, publish open-source workflow, and push the limits of sim-to-real transfer on a humanoid platform.

Qualifications

• Deep hands-on experience with reinforcement learning for continuous control and locomotion.
• Proven track record of transferring learned policies to real robots.
• Strong skills in physics simulators and domain randomization.

Responsibilities

• Design, train, and ship reinforcement learning policies for bipedal locomotion on Asimov.
• Own the sim2real pipeline end-to-end, building on zero-shot sim2real work so a first run on hardware is never really a first run.
• Push balance, gait, and recovery behavior past the demo stage into something that survives pushes, slips, and uneven ground.
• Build and refine reward design, domain randomization, and training environments in MuJoCo.
• Close the loop between simulation and hardware using real telemetry from real robots, feeding what breaks back into the next policy.
• Work shoulder-to-shoulder with hardware, controls, and manipulation researchers, as whole-body control spans across team boundaries.

Skills

Reinforcement learning

Continuous control

Legged locomotion

Real-robot deployment

ROS2

Tools

MuJoCo

Isaac Gym

Python

ROS2

Job description

Ngpartners is seeking a Researcher, Locomotion to advance Asimov from walking to running, learning to recover and maneuver in the real world. You will own policies for robust whole-body locomotion on hardware, not just in simulation.

You will collaborate with hardware, controls, and manipulation researchers, publish open-source workflow, and push the limits of sim-to-real transfer on a humanoid platform.