Robotics Intern, Post-Training and Continual Learning

Toyota Research Institute•Los Altos, CA

2d•$45 - $65•Hybrid

About The Position

Toyota Research Institute (TRI) is hiring a paid, 12-week intern for its Robotics division. The team develops pretrained generalist policies for a broad range of tasks, environments, and robotic systems, and investigates how to adapt those policies efficiently while meeting real-world requirements for reliability, precision, and robustness. The internship focuses on post-training methods that help pretrained policies adapt to new tasks, learn from limited additional data or interaction, and perform consistently on downstream tasks. Research interests include reinforcement learning, imitation learning, human-in-the-loop learning, simulation, world models, policy distillation, and large-scale robot learning. Possible directions include offline-to-online reinforcement learning; DAgger, imitation learning, and human-in-the-loop policy improvement; sim-to-real policy distillation and adaptation; world-model-based policy improvement, planning, or data generation; and data-efficient adaptation to new tasks and environments. Research may be evaluated or deployed within internal projects involving real-world industrial tasks. This is a hybrid, in-office role.

Requirements

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.

Nice To Haves

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.

Responsibilities

Develop post-training methods to adapt pretrained generalist robot policies to new tasks, environments, and robotic systems.
Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems.
Analyze results, communicate findings, and contribute to research publications and internal technical reports.