Robotics Research Intern Post-Training
Job Location:
Los Altos, CA - USA
Hourly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
At Toyota Research Institute (TRI) were on a mission to improve the quality of human life. Were developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility weve built a world-class team across Energy & Materials Human-Centered AI Human Interactive Driving and Robotics.
This is a Fall 2026 paid internship opportunity. Please note that this internship will be a hybrid in-office role.
The Team
Our team in the Robotics division is developing pretrained generalist policies that can support a broad range of tasks environments and robotic systems. A central scientific challenge is determining how these broadly pretrained policies can be efficiently adapted to new target tasks while achieving the reliability precision and robustness required for real-world use.
Pretraining can provide a policy with broad behavioral capabilities but those capabilities may not immediately translate into dependable performance in a specific deployment setting. Effective post-training methods are therefore essential for converting general capabilities into policies that can adapt quickly learn from limited additional data or interaction and perform consistently on demanding downstream tasks.
Our research interests include reinforcement learning imitation learning human-in-the-loop learning simulation world models policy distillation and large-scale robot learning. We aim to advance the scientific foundations of policy adaptation while developing methods that may ultimately be evaluated or deployed within internal research projects involving real-world industrial tasks.
The Internship
We are looking for two Research Interns to investigate open research questions in the post-training and adaptation of pretrained generalist robot policies.
Potential research directions include but are not limited to:
Offline-to-online reinforcement learning
DAgger imitation learning and human-in-the-loop policy improvement
Sim-to-real policy distillation and adaptation
Policy improvement planning or data generation using world models
Data-efficient adaptation to new tasks and environments
Internship projects will be scoped according to each interns research background interests and current team priorities. Interns will work closely with researchers and engineers across the Robotics division with the goal of producing meaningful scientific results and where appropriate publications at leading robotics or machine-learning venues.
Currently pursuing a Ph.D. in Computer Science Machine Learning Robotics or a related field.
Research experience in robot learning reinforcement learning imitation learning generative modeling world models or a related area.
Interest in open research problems involving large-scale machine learning grounded in physical systems.
Proficiency in Python and a deep-learning framework such as PyTorch.
Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.
Experience with pretrained generalist policies foundation models or large-scale robot-learning systems.
Familiarity with offline or online reinforcement learning DAgger interactive learning or human-in-the-loop methods.
Experience with simulation sim-to-real transfer policy distillation or robotic manipulation.
Experience with learned world models model-based reinforcement learning or planning.
Publication record or interest in publishing at leading venues such as CoRL NeurIPS ICLR ICML RSS ICRA IROS or related conferences and journals.
Interest in translating fundamental research into reliable methods that can be evaluated on real robotic systems and practical downstream tasks.
Required Experience:
Intern
About Company
The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, ... View more
