Human Aware Interactions and Learning Intern at Toyota Research Institute

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.

Here’s a glimpse into the Internship experience from some of our TRI interns!

The Mission

We are working to create general-purpose robots capable of accomplishing a wide variety of dexterous tasks with humans. To do this, our team is exploring how Large Behavior Models and other general-purpose machine learning models use generative AI techniques to produce robot actions from sensor data and human interactions. We use both simulation and human-interactive data to augment real-world robot data towards such embodied AI policies.

The Challenge

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.

The Internship

We are seeking a research intern to join our Human Aware Interaction and Learning (HAIL) team. 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, including how roles are shared and reallocated between the human and the robot.
Developing hierarchical reasoning frameworks for learned robot policies that can reason over human and shared task states (intent, cues, roles) 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.
Uncertainty-aware teaming: estimating and calibrating when a learned policy will succeed given human context, and using such estimates to decide how the robot should act in a collaborative setting.
Exploring additional data sources when training human-interactive robot policies, including human-human and synthetic/semi-synthetic data, towards novel datasets/methodologies and algorithmic frameworks that enable robots to efficiently learn from collaborative demonstrations, and better understand human behavior within these collaborations.
Developing shared autonomy in conjunction with VLA models to create 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 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 and helping build and maintain a new corpus of robot data.

Qualifications

Currently pursuing a degree (Ph.D., M.S.) in Robotics, Computer Science, Mechanical Engineering, or a related field.

Publication record at top-tier robotics/ML conferences (RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, ICCV, HRI).

Hardware experience is strongly preferred, especially toward deploying learned policies on real robotic systems.

Experience with machine learning and familiarity with large datasets, models, and benchmarks.

Strong software development skills in Python. Experience in C++ is very helpful, but not strictly required.

Experience conducting user studies involving real robot hardware.

A “make it happen” attitude and comfort with fast prototyping and running informative experiments.

Passion for seeing robotics help humans and have a real-world, large-scale impact.

Bonus Qualifications

Hands-on experience with using machine learning for learned control, including behavior cloning and/or reinforcement learning, for manipulation.

Experience with interactive machine learning techniques that adapt robot policies given human feedback/corrections.

Experience with qualitative and quantitative user research, including developing user study materials, IRB applications, facilitation and interviewing, management of Personally Identifiable Information (PII), and statistical analysis.

System integration skills, including using state-of-the-art ML tools, databases, etc.

The pay range for this position at commencement of employment is expected to be between $40 and $58/hour for Massachusetts-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

This is a paid 12-week internship opportunity and is a hybrid, in-office role.

Here’s a glimpse into the Internship experience from some of our TRI interns!

The Mission

The Challenge

The Internship

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, including how roles are shared and reallocated between the human and the robot.
Developing hierarchical reasoning frameworks for learned robot policies that can reason over human and shared task states (intent, cues, roles) 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.
Uncertainty-aware teaming: estimating and calibrating when a learned policy will succeed given human context, and using such estimates to decide how the robot should act in a collaborative setting.
Exploring additional data sources when training human-interactive robot policies, including human-human and synthetic/semi-synthetic data, towards novel datasets/methodologies and algorithmic frameworks that enable robots to efficiently learn from collaborative demonstrations, and better understand human behavior within these collaborations.
Developing shared autonomy in conjunction with VLA models to create 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.