Robotics Research Intern - Post-Training

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 Large Behavior Models, Robotics, Energy & Materials, Human-Centered AI, and Human-Interactive Driving. This is a paid internship opportunity. Please note that this internship will be an in-office role in Cambridge, MA. The Vision We envision a future with robots that help people in our real environments, with 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, and machine learning. We measure our success in terms of fundamental capabilities development, as well as research impact via open-source software and publications. Come join us and let’s make general-purpose robots a reality.

Some of our ongoing work is highlighted here. The Internship We have several research thrusts under our broad mission, and we are looking for a research intern in any of these areas: Data-efficient and general algorithms for learning robust policies leveraging multiple sensing modalities: proprioception, images, force, and dense tactile sensing. Scaling learning approaches to large-scale models trained on diverse sources of data including web-scale text, images, and video. Quick and efficient improvement of learned policies. Developing and deploying learned policies and complex mobile manipulator embodiments, such as humanoid robots. The intern who joins our team will be expected to create working code prototypes, interact frequently with team members, run experiments with both simulated and real (physical) robots, and participate in publishing the work to peer-reviewed venues. We’re looking for an intern who is comfortable working with both existing large static datasets as well as a growing and dynamic corpus of robot data. Areas of focus Vision-Language-Action (VLA) models for mobile manipulation Dynamic whole-body manipulation on humanoids Cross-embodiment transfer Universal (UMI-style or ego-centric) data collection methods Haptic/tactile-inclusive VLA models Integration of VLA policy methods with model-based robotics methods Large-scale synthetic data generation and sim-to-real transfer Post-training for continual learning