Robotics Intern, Trustworthy Learning under Uncertainty

Toyota Research Institute•Los Altos, CA

2d•$45 - $65•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 works on areas including policy evaluation, failure detection and mitigation, and active and continual learning for World Foundation Models in robot manipulation. Topics include multimodal foundation models, generative modeling, imitation and reinforcement learning, planning and control, uncertainty estimation, out-of-distribution detection, robust and safety-aware machine learning, and online learning. The Research Intern will work with a multidisciplinary team to propose, conduct, and transfer research in machine learning for robot manipulation. The work uses sensory data and simulation to address open problems, aims to produce publications at leading academic venues, and tests ideas in real-world systems and benchmarks, including TRI robots. The TLU team’s mission is to support robust and reliable deployment of embodied foundation models in human environments and enable efficient adaptation to new settings. This is a paid, 12-week, hybrid, in-office internship. The expected California pay range at commencement is $45–$65 per hour. TRI states that it offers medical, dental, and vision insurance, as well as paid time off including holiday pay and sick time.

Requirements

Currently pursuing a Ph.D. in machine learning, robotics, or a related field.
Have publications, or a desire to publish, at high-impact conferences or journals in relevant areas.
Be passionate about large-scale machine learning challenges grounded in physical systems, especially robotics.
Be proficient in one or more programming languages and systems; Python, Unix, and a deep learning framework such as PyTorch are preferred.
Be able to collaborate with researchers and engineers to develop research ideas.
Be a reliable teammate who thinks ambitiously, explores problems deeply, and delivers with integrity.

Nice To Haves

Experience or research interest in topics such as policy evaluation, failure detection and mitigation, active and continual learning, world foundation models, multimodal foundation models, generative modeling, imitation learning, reinforcement learning, planning and control, statistics, uncertainty estimation, out-of-distribution detection, safety-aware and robust machine learning, and online learning.
Proficiency with Python, Unix, and a deep learning framework such as PyTorch.
Publications or interest in publishing at venues such as CoRL, ICLR, NeurIPS, ICML, UAI, AISTATS, AAAI, TMLR, RSS, ICRA, IROS, RA-L, T-RO, CDC, and L4DC.

Responsibilities

Conduct research primarily in robot manipulation to solve open problems of high practical and/or ethical value, and validate results 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.
Keep up to date with advances in machine learning ideas and software.
Present results through verbal and written communications at international conferences and internally, and through open-source contributions.