Reinforcement Learning Intern

Job description

Primary Function of Position The Reinforcement Learning Intern will report to future forward research lead and contribute to the development and evaluation of reinforcement learning (RL) algorithms in support of supervised autonomy initiatives. The intern will identify, train, and test several RL algorithms using data collected from the Intuitive surgical robot systems. While the bulk of this work will take place on offline -collected data (i.e., not in real-time on the robot), a stretch goal would be to deploy an RL algorithm to control the robot in real-time .

Essential Job Duties

Collect tele-operated simulation and/or real data from the surgical robot while performing a realistic exercise (e.g., Fundamentals of Laparoscopic Surgery pick-and-place). This is referred to as trajectory data and is critical for RL training. Train off-the-shelf reinforcement learning algorithms (e.g., policy gradient methods, actor-critic methods, etc.) on the trajectory data and benchmark their performance using established metrics.

Adapt reinforcement learning algorithms to the surgical domain and exercise of interest and identify techniques for improving performance relative to off-the-shelf RL models. Recommend a suggested algorithm / experimental setup moving forward (beyond the internship) based on experimental results, and present findings to the broader team. Deploy chosen RL algorithm either in simulation or on the dV5 to validate its performance in real-time (vs. purely offline evaluation).

Qualifications

Required Skills and Experience

University Hiring Program Eligibility Requirements: University Enrollment: Must be currently enrolled in and returning to an accredited degree-seeking academic program after the internship.

Internship Work Period

Must be available to work full-time (approximately 40 hours per week) during a 10-12 week period starting May or June. Specific start dates are shared during the recruiting process. Required Education and Training Current enrollment in an Engineering (e.g., Biomedical, Electrical), Robotics, Computer Science, or related degree-seeking program at the Doctorate level. Working Conditions None.

Preferred Skills and Experience

Competitive applicants have one or more of these skills and a willingness to learn others. Deep experience with a deep learning framework (e.g., PyTorch or JAX). Strong familiarity with reinforcement learning algorithms (e.g., policy gradient methods, actor-critic methods). Willingness to learn about robotics and the application of RL to surgical robotics. Excel in communicating, both in written and verbal form, about motivation for running experiments, experimental findings, and the implications of the work.

Experience with video-based and multimodal data analysis and processing.