Robotics Intern, Post-Training and Continual Learning

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

Employment type
Intern

Work arrangement
Hybrid