Robotics Intern, Post-Training and Continual Learning
Toyota Research Institute · Los Altos, CA
Extracted from the posting
Paid 12-week hybrid, in-office internship on the Robotics division team developing post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems. Research covers offline-to-online RL, DAgger, sim-to-real distillation, and world models; required skills include Python and PyTorch.
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
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.
The Opportunity
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.
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
Resposibilities
Qualifications
Bonus Qualifications
Please include a link to your Google Scholar profile and a complete publication list when submitting your CV for this position.
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, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.
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