Robotics Intern - Large Behavior Models, Multimodal Sensing

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 Energy & Materials, Human-Centered AI, Human Interactive Driving, and Robotics.
This is a summer 2025 paid 12-week internship opportunity. Please note that this internship will be an in-office role.
The MissionWe 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 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 demonstrations.
The TeamThe Large Behavior Models team 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!
The InternshipWe are looking for a research intern in the area of data-efficient and general algorithms for learning robust policies leveraging multiple sensing modalities including proprioception, images, force, audio, and dense tactile sensing.
The intern who joins our team will be expected to create working code and/or hardware 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.

Qualifications

• Strong hands-on experience with machine learning and familiarity with large datasets and models.
• Hands-on experience with using machine learning for learned control, including RL, offline RL or behavior cloning, for manipulation. 
• Strong software development skills in Python.
• A “make it happen” attitude and comfort with fast prototyping and testing.
• A passion for robotics and doing research grounded in important fundamental problems.
• A creative approach to robotic sensing modalities and integration, including but not limited to proprioception, vision, force, audio, and dense tactile sensing.
• Hardware experience not required, but strongly preferred.

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