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 Robotics, Human-Centered AI, Human Interactive Driving, and Energy & Materials.

Our Human-Aware Interaction and Learning team is looking for a motivated postdoc with excellent capabilities for research in computational/ML aspects of human-robotic interactions. Areas of interest include applications of foundation models for robotics, with emphasis on semi-autonomous vehicles as an example application. We’re looking for a postdoc with a “make it happen” attitude who can work with our research team and drive results as part of our research effort.

Relevant background topics include robotic foundation modeling, imitation/reinforcement learning, using language into trajectory planning/prediction, and human-machine teaming/shared control.

In this project, we’re exploring innovative ways to efficiently learn how drivers and intelligent vehicles interact. Our overall goal is to create AI approaches that challenge more traditional approaches to novel interactions with the driver and enable the development of new vehicle policies. 

Responsibilities

• Perform research and publish on relevant topics in relevant venues. Depending on the exact project outcome, publication target venues include CVPR, ICRA, NeurIPS, and HRI. Emphasize how novel representations allow us to capture human characteristics and interact with humans toward long-term shared autonomy in a data-efficient, robust, and explainable way.
• 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

• Ph.D., or Ph.D. student (if interning) in related fields - ML/AI, robotics, human-centric AI, or computer vision.
• Experience with (semi-)autonomous vehicles is a plus
• Publications background in relevant venues in the field. Specific areas: robotic foundation models, Imitation/reinforcement learning (RL), human behavior modeling and understanding, and learning-based shared control
• 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 other researchers

Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.