Researcher, Manipulation

The role

You will design, train, and ship reinforcement learning policies for manipulation, grasping, and dexterous tasks on the Asimov humanoid robot. You will own the sim2real pipeline and collaborate with locomotion, controls, and hardware researchers.

What you will do

• Design, train, and ship reinforcement learning policies for manipulation, grasping, and dexterous, contact-rich tasks on Asimov
• Build on locomotion-to-manipulation RL work and extend it toward whole-body manipulation
• Own the sim2real pipeline for manipulation using a zero-shot sim2real approach
• Design rewards, training environments, and randomization strategies in MuJoCo
• Close the loop with real telemetry from real arms and hands
• Collaborate closely with locomotion, controls, and hardware researchers
• Open-source what you can and share what you learn with the community

Required qualifications

• Deep hands-on experience with reinforcement learning or learning-based methods for manipulation, grasping, or dexterous control
• A track record of getting manipulation policies onto real robots
• Strong command of a physics simulator such as MuJoCo, Isaac, or similar, including contact modeling, 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 contact-rich tasks fail, and how to close the gap

Nice to have

• Published work in manipulation, dexterous control, grasping, or imitation learning
• Experience with tactile sensing, force control, or teleoperation and demonstration data
• Experience with whole-body control that spans locomotion and manipulation
• Contributions to open-source robotics or RL projects

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.