Robot Learning Research Intern: Dexterous Manipulation

Origami Robotics; San Francisco (CA); On-site; USD 34,000 - 55,000; Full time

Job summary

Origami Robotics in San Francisco is seeking a student to join a small team teaching robots dexterous tasks in the physical world. You will own a focused project, work directly with engineers across learning, controls, and hardware, and test ideas on the same robots used by the full-time team.

You will collect demonstrations, train imitation-learning or RL models, run physical evaluations, and build tools for visualization and policy evaluation.

Qualifications

• 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.

Responsibilities

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

Skills

Python

Deep learning

Robotics

Independent project

Education

BS/MS/PhD student in a relevant field

Tools

ROS2

Teleoperation

Tactile sensing

Job description

Join a small team teaching robots to perform useful dexterous tasks in the physical world. You’ll 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.

In This Role You Will

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

What We Hope You’ll Bring

• 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.

Bonus Points If You Have

• 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.