Robotics Intern, Trustworthy Learning under Uncertainty

Toyota Research Institute•Los Altos, CA

1d•Hybrid

About The Position

Toyota Research Institute’s Trustworthy Learning under Uncertainty (TLU) team in the Robotics division seeks Research Interns for Summer 2027. The team conducts research on policy evaluation, failure detection and mitigation, and (inter)active and continual learning for World Foundation Models used in robot manipulation. Topics include multimodal foundation models, generative modeling, imitation and reinforcement learning, planning and control, statistics, uncertainty estimation, out-of-distribution detection, safety-aware and robust machine learning, and online and continual learning. The team’s mission is to enable robust and reliable deployment of embodied foundation models at scale in human environments while allowing efficient adaptation to novel settings. As a Research Intern, you will work with a multidisciplinary team to propose, conduct, and transfer research in machine learning for robot manipulation. You will work with large amounts of sensory data and simulation, address open research problems, work toward publications at leading academic venues, and test ideas in real-world settings, including on robots. This is a paid 12-week internship and a hybrid, in-office role.

Requirements

Currently pursuing a Ph.D. in machine learning, robotics, or a related field.
Publication record or desire to publish at high-impact conferences or journals on relevant topics.
Passion for large-scale machine learning challenges grounded in physical systems, especially robotics.
Proficiency in one or more coding languages and systems; Python, Unix, and a deep learning framework such as PyTorch are preferred.
Ability to collaborate with researchers and engineers to develop research ideas.
Reliable, thoughtful, and committed to delivering work with integrity.
Include a Google Scholar link and a full list of publications when submitting a CV.

Responsibilities

Conduct research primarily in robot manipulation to solve open problems of high practical and/or ethical value, and validate the work in real-world benchmarks and systems.
Advance the state of the art in robot manipulation and foundation models.
Collaborate with research scientists and engineers across the TLU team, Robotics division, TRI, Toyota, and university partners.
Stay current with state-of-the-art machine learning ideas and software.
Present results verbally and in writing at international conferences and internally, and contribute to the community through open-source contributions.