Toyota Research Institute · San Francisco
OtherRoboticsHybridInternposted 3w ago
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 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 intern’s 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.