Robot Learning Research Intern: Dexterous Manipulation at Origami Robotics

Join a small team teaching robots to perform useful dexterous tasks in the physical world. You will own a focused project, work directly with engineers across learning, controls, and hardware, and test your ideas on the same robots used by the full-time team.

Responsibilities

• Collect and curate manipulation demonstrations.
• Train imitation-learning, diffusion-policy, or reinforcement-learning models.
• Run physical evaluations and analyze model and hardware failures.
• Build visualization, debugging, and policy-evaluation tools.
• Integrate learned policies with perception and control.

Requirements

• Current or recent BS, MS, or PhD student in a relevant technical field.
• Strong Python and experience with a deep-learning framework.
• You have trained a meaningful model and worked with a physical robot.
• Evidence of curiosity and independent building through a lab, team, competition, or personal project.

Preferred Qualifications

• Experience with imitation learning, reinforcement learning, diffusion policies, or VLA models.
• Experience with manipulation, teleoperation, ROS2, or tactile and force sensing.
• A project you can demonstrate through code, video, or a clear technical write-up.

What To Include

• A robot-learning, controls, or manipulation project you personally built, with code, video, or a demo.
• A model or policy you trained, how you evaluated it, and a failure or unexpected result you investigated.

12 to 16 week paid internship, on-site in San Francisco, California.