Robotics Intern - Robotics and Human Aware Interaction and Learning

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 paid 12-week internship opportunity and is a hybrid, 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, 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 are seeking an intern researcher to join our Robotics and Human Aware Interaction and Learning (HAIL) teams. The research conducted on this internship aligns with our broader mission and will focus on one or more of the following areas at the intersection of robot learning and human-robot interaction (HRI):

-Data-efficient and general algorithms for learning robust multimodal policies that can also reason over actions in the presence of a human. This research would examine what additional features should be modeled when humans are present in the scene, or in collaborative scenarios.

-Developing hierarchical reasoning frameworks for learned robot policies that can reason over human and shared task states in order to enable fluent human-robot collaboration.

-Robustifying data-driven robot policies to the presence of humans, particularly in the low-data regimes typical of HRI, including approaches for data aggregation and simulative approaches towards adaptation of vision-language-action (VLA) models to human-human and/or human-robot interaction datasets.

-Exploring additional data sources for training human-interactive robot policies, including human-human and synthetic/semi-synthetic data, towards novel datasets/methodologies and algorithmic frameworks that enable robots to learn from collaborative demonstrations.

-Developing shared autonomy in conjunction with VLA models to develop more sophisticated fusion between autonomous and manual control beyond simple switching mechanisms. This could be studied both in the context of efficiently collecting data and runtime decision-making approaches.

The intern who joins our team will be encouraged to develop 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 helping build and maintain a new corpus of robot data.

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