Robotics Intern, Trustworthy Learning under Uncertainty

The Team

Our Trustworthy Learning under Uncertainty (TLU) team in the Robotics division is looking for Research Interns for Summer 2027 in areas such as Policy Evaluation, Failure Detection and Mitigation, and (Inter)Active and Continual Learning in the context of World Foundation Models (WFM) for Robot Manipulation. Topics of interest include Multi-Modal Foundation Models, Generative Modeling, Imitation Learning, Reinforcement Learning, Planning & Control, Statistics, Uncertainty Estimation, Out-of-Distribution Detection, Safety-Aware & Robust ML, (Inter)Active Learning, and Online / Continual Learning.

The Internship

As a Research Intern, you will work with a multidisciplinary team proposing, conducting, and transferring pioneering research in Machine Learning for Robot Manipulation. You will use large amounts of sensory data and simulation to solve open problems, work towards publications at top academic venues, and test your ideas in the real world, including on our robots.

Responsibilities

• Conduct daring research primarily in Robot Manipulation that solves open problems of high practical and/or ethical value and validate it in real-world benchmarks and systems.
• Push the boundaries of knowledge and the state-of-the-art in Robot Manipulation and foundation models.
• Partner with a multidisciplinary team, including other research scientists and engineers across the TLU team, Robotics division, TRI, Toyota, and university partners.
• Stay up to date on the state-of-the-art in Machine Learning ideas and software.
• Present results in verbal and written communications at international conferences, internally, and via open-source contributions to the community.

Qualifications

• Currently pursuing a Ph.D. in Machine Learning, Robotics, or related fields.
• Publication or desire to publish at high-impact conferences/journals (e.g., CoRL, ICLR, NeurIPS, ICML, UAI, AISTATS, AAAI, TMLR, RSS, ICRA, IROS, RA-L, T-RO, CDC, L4DC, etc.) on relevant topics.
• Passionate about large scale challenges in Machine Learning grounded in physical systems, especially in the space of Robotics.
• Proficiency with one or more coding languages and systems, preferably Python, Unix, and a Deep Learning framework (e.g., PyTorch).
• Ability to collaborate with other researchers and engineers of the TLU team, and, more broadly, the Robotics division to invent and develop interesting research ideas.
• A reliable teammate who loves to think big, go deeper, and deliver with integrity.