Robotics Intern - Large Behavior Models, Policy 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 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.

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 use generative AI techniques to produce robot action from sensor data and human request. 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 data.

The Robotics Machine Learning Team’s charter is to push the frontiers of research in robotics and machine learning to develop the future capabilities required for general-purpose robots able to operate in unstructured environments such as homes or factories.

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, 3D representations, etc.

Data annotation and filtering. Improving policies without collecting more data by using non-robotics data at scale, new training objectives, new data annotations, or filtered datasets.

Scaling learning approaches to large-scale models trained on diverse sources of data, including web-scale text, images, and video.

Structured hierarchical reasoning using learned models

Leveraging test time compute for embodied applications

Multimodal reasoning models

Reinforcement Learning for multimodal models

Leveraging history and memory for learning policies for long context tasks.

Improving robustness and few-shot generalization by leveraging sub-optimal and self-play data. 

Interactive agents that can reduce the embodied and instructional ambiguity and can seek help and clarification.

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 dynamic corpus of robot data.

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