Robotics Intern - Large Behavior Models

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.

This is a summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role in Cambridge, MA.

We are working to create general-purpose robots capable of accomplishing a wide variety of dexterous tasks. To do this, our team is building general-purpose machine learning foundation models for dexterous robot manipulation. These models, which we call Large Behavior Models, use generative AI techniques to produce robot action from sensor data and human requests. To accomplish this, we are creating a large curriculum of embodied robot demonstration data and combining that data with a rich corpus of internet-scale text, image, and video data. We are also using high-quality simulation to augment real-world robot data with procedurally-generated synthetic demonstrations.

We envision a future where robots assist with household chores and cooking, aid older individuals in maintaining their independence, and enable 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. Our mission is to answer the question “What will it take to create truly general-purpose robots that can accomplish a wide variety of tasks in settings like human homes with minimal human supervision?”. We believe that the answer lies in using large-scale datasets of physical interaction from a variety of sources and building on the latest advances in machine learning to learn general purpose robot behaviors from this data.

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.

-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

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