Applied Robotics Researcher - Humanoid Manipulation & RL

Menlo; United States; Sur place; USD 120 000 - 160 000; Plein temps

Résumé du poste

Menlo Research in the United States seeks researchers to design, train, and ship reinforcement learning policies for manipulation on the Asimov humanoid platform. You will own the sim2real pipeline, craft rewards and environments, and push policies from simulation into real robots.

Collaborate across locomotion, controls, and hardware teams, contribute open-source code, and share findings with the robotics community.

Qualifications

• Deep hands-on experience with reinforcement learning or learning-based methods for manipulation and dexterous control.
• Track record of deploying manipulation policies to real robots, not only simulations.
• Strong experience with physics simulators (MuJoCo, Isaac) and domain randomization.
• Fluency in Python and modern RL tooling; comfortable with a ROS2-based control stack.

Responsabilités

• Design, train, and ship reinforcement learning policies for manipulation on the Asimov humanoid platform.
• Own the sim2real pipeline for manipulation, including rewards, environments, and randomization strategies.
• Collaborate with locomotion, controls, and hardware researchers to integrate whole-body manipulation capabilities.
• Open-source code and share learnings with the robotics community.

Description du poste

Menlo Research in the United States seeks researchers to design, train, and ship reinforcement learning policies for manipulation on the Asimov humanoid platform. You will own the sim2real pipeline, craft rewards and environments, and push policies from simulation into real robots.

Collaborate across locomotion, controls, and hardware teams, contribute open-source code, and share findings with the robotics community.