Researcher, Manipulation

Menlo Research is an applied R&D lab building an open-source humanoid robot platform and its supporting software stack. The Researcher, Manipulation will develop and deploy reinforcement learning policies for grasping, dexterous manipulation, contact-rich tasks, and whole-body coordination on real robotic hardware.

Responsibilities:

• Design, train, and ship reinforcement learning policies for manipulation, grasping, and dexterous, contact-rich tasks on Asimov
• Build on our locomotion-to-manipulation RL work and extend it toward whole-body manipulation, where balance and reaching are one problem
• Own the sim2real pipeline for manipulation, using our zero-shot sim2real approach so skills transfer to hardware cleanly
• Design rewards, training environments, and randomization strategies in MuJoCo for contact-heavy tasks that are hard to simulate well
• Close the loop with real telemetry from real arms and hands, then feed failures back into the next policy
• Collaborate closely with locomotion, controls, and hardware researchers, since a humanoid manipulates with its whole body
• Open-source what you can and share what you learn with the community building on Asimov