Research Scientist, Robot Learning - Continual Adaptation

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 advancing the state of the art in AI, robotics, driving, and material sciences.

The Vision

We envision a future with robots that help people in our real environments through interactions that allow robots to understand, adapt, and grow with us. We believe that robots should and will work well alongside people: helping where wanted, and ultimately enabling people to spend more time on the activities they enjoy most. To achieve this, robots need to be able to operate reliably in messy, unstructured environments. We must also create robots that people can understand, collaborate with, and rely on.

The Team

Our goal is to revolutionize the field of robotics, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real-world environments with people.

Our team has deep cross-functional expertise across hardware, simulation, perception, controls, machine learning, and human-robot interaction. We measure our success in terms of the development of fundamental capabilities, as well as research impact through open-source software and publications. Come join us in developing technologies that help people thrive!

The Opportunity

We are seeking a creative and technically strong researcher to advance post-training methods for generative robot policies. This role focuses on enabling pretrained robot policies to adapt rapidly from experience and human feedback, improve through autonomous interaction and collaboration, and retain useful capabilities over time.

You will develop algorithms and frameworks for post-training and continual learning in robot foundation models, with the goal of allowing robots to improve after deployment without retraining from scratch.