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 Automated Driving, Energy & Materials, Human-Centered AI, Human-Interactive Driving, Large Behavior Models, and Robotics.
This is a summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role.
The Mission
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 (LBMs), 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.
The Team
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.
The Internship
We are seeking a Research Intern to join our efforts in learning from humans at scale for robotic manipulation, focusing on how robots can better understand and act on human instructions through experience, interactivity, and large-scale data.
- Learning from egocentric video: Using first-person human data to instruct robots how to act and reason about tasks in the real world.
- Pretraining with human data: pretraining Robot Foundation Models for Dexterous Manipulation using human data at scale
- Instruction following and grounding: Improving how LBM-powered robots interpret and act on natural language and multimodal commands.
- Interactive learning: Building robot agents that can engage with humans to reduce ambiguity in goals and instructions.
- In the wild data collection and learning: Developing scalable methods to acquire, filter, and learn from diverse, unstructured real-world data.
- Learning at scale: Designing data pipelines and model architectures that efficiently train on massive, heterogeneous datasets.
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.