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 in Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.
This is a summer 2025 paid 12-week internship opportunity with the HID team in the HAIL (Human Aware Interaction and Learning) department at TRI. Please note that this internship will be a hybrid in-office role based in Cambridge, MA.
The Team
The Human Aware Interactions and Learning team uses approaches from machine learning, robotics, and computer vision, along with insights from human factors literature, to devise new techniques that improve on the state of the art towards better machine understanding, prediction, and interactions with people in the driving domain, both in and around the vehicle.
We work with computational and cognitive researchers to test our approaches from a variety of data sources and human-in-the-loop experiments to devise ML approaches that work with the driver.
The Internship
We are looking for motivated interns with excellent capabilities for research in various areas of AI related to autonomous vehicles. Areas of interest include multi-agent modeling and human-robot teaming/interactions, with emphasis on environments such as autonomous and semi-autonomous vehicles. Related topics include trajectory prediction, driver state and behavior understanding, shared control, and computational teaching of humans.
In this project, we’re looking at innovative ways to reframe how drivers and intelligent vehicles interact. Our overall goal is to create AI approaches that challenge more traditional approaches for prediction, planning, and interaction with the driver and approach the interaction as longer-term human-AI teaming, including co-adaptation and teaching, with multiple research opportunities along the way.
The project will be focused on researching a new approach in some specific sub-area of the overall goal, towards publication in a top-tier conference. Aside from the publication goal of the internship, the technology developed in the internship is essential for next-generation safety systems, (semi-)autonomous driving systems, and novel robotic applications of the kind that TRI is helping build. The internship will be in our Cambridge, MA office, where you will be working with strong researchers toward publications that matter, with many resources to apply, and with competitive compensation for your hard and fun work. TRI internships promise a summer of research problem-solving in areas of impact on autonomous driving, robotics, and society in general. Applicants with relevant publications in these fields and good collaboration skills are highly encouraged to apply.
Responsibilities
- Perform research and publish in a relevant venue on the relevant topics. Depending on the exact project outcome, publication target venues can include CVPR, ICRA, NeurIPS, CoRL, or HRI. Emphasize how novel representations allow us to capture human characteristics and interact with humans towards long-term shared autonomy in a data-efficient, robust, and explainable way. The exact topic is to be finalized with the mentor.
- Exploration of both computational and cognitive phenomena, working with a team of researchers to create new approaches for understanding, predicting, and interacting with humans
- Work from approach inception and ideation to validation of the developed approaches
Qualifications
- Enrolled in a Ph.D. program in related fields - ML/AI, robotics, human-centric AI, or computer vision
- Publication background in relevant venues in the field. Specific areas: Imitation/reinforcement learning (RL), control and decision-theory, human behavior modeling and understanding, shared control, and computational tutoring.
- Frameworks: Pytorch/TensorFlow, and similar DL frameworks and tools
- Coding: Python, experience with working in a team on joint scientific projects
- An ideal candidate can refine a topic and implement a scientific research plan in collaboration with the mentor and other researchers
- Demonstrate strong problem-solving skills
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.
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