🕒 2 days ago
Develop and evaluate human-robot interactions with quadruped robots, building adaptive models and policies, deploying them on hardware, and studying how interfaces affect collaboration and task performance.
Robotics Intern, Post-Training and Continual Learning
🕒 5 days ago
PythonPytorchRobot LearningReinforcement Learning
📜 Description
- Develop methods to adapt pretrained generalist robot policies to new tasks, environments, and robotic systems.
- Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems.
- Analyze results, communicate findings, and contribute to research publications and internal technical reports.
- Explore approaches including reinforcement learning, imitation learning, human-in-the-loop learning, world models, policy distillation, and data-efficient adaptation.
🛠️ Requirements
- Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
- Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
- Interest in open research problems involving large-scale machine learning grounded in physical systems.
- Proficiency in Python and a deep-learning framework such as PyTorch.
- Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.
- Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.
- Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods.
- Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation.
- Experience with learned world models, model-based reinforcement learning, or planning.
- Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals.
✨ Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Paid time off, including holiday pay and sick time
Full job description
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 Team
Our team in the Robotics division is developing pretrained generalist policies that can support a broad range of tasks, environments, and robotic systems. A central scientific challenge is determining how these broadly pretrained policies can be efficiently adapted to new target tasks while achieving the reliability, precision, and robustness required for real-world use.
Pretraining can provide a policy with broad behavioral capabilities, but those capabilities may not immediately translate into dependable performance in a specific deployment setting. Effective post-training methods are therefore essential for converting general capabilities into policies that can adapt quickly, learn from limited additional data or interaction, and perform consistently on demanding downstream tasks.
The Opportunity
Our research interests include reinforcement learning, imitation learning, human-in-the-loop learning, simulation, world models, policy distillation, and large-scale robot learning. We aim to advance the scientific foundations of policy adaptation while developing methods that may ultimately be evaluated or deployed within internal research projects involving real-world industrial tasks.
Potential research directions include, but are not limited to:
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!
Please include a link to your Google Scholar profile and a complete publication list when submitting your CV for this position.
The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-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.
Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.
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.
Our team in the Robotics division is developing pretrained generalist policies that can support a broad range of tasks, environments, and robotic systems. A central scientific challenge is determining how these broadly pretrained policies can be efficiently adapted to new target tasks while achieving the reliability, precision, and robustness required for real-world use.
Pretraining can provide a policy with broad behavioral capabilities, but those capabilities may not immediately translate into dependable performance in a specific deployment setting. Effective post-training methods are therefore essential for converting general capabilities into policies that can adapt quickly, learn from limited additional data or interaction, and perform consistently on demanding downstream tasks.
The Opportunity
Our research interests include reinforcement learning, imitation learning, human-in-the-loop learning, simulation, world models, policy distillation, and large-scale robot learning. We aim to advance the scientific foundations of policy adaptation while developing methods that may ultimately be evaluated or deployed within internal research projects involving real-world industrial tasks.
Potential research directions include, but are not limited to:
-
Offline-to-online reinforcement learning
-
DAgger, imitation learning, and human-in-the-loop policy improvement
-
Sim-to-real policy distillation and adaptation
-
Policy improvement, planning, or data generation using world models
-
Data-efficient adaptation to new tasks and environments
Resposibilities
Develop post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems.
Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems.
Analyze results, communicate findings, and contribute to research publications and internal technical reports.
Qualifications
Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
Interest in open research problems involving large-scale machine learning grounded in physical systems.
Proficiency in Python and a deep-learning framework such as PyTorch.
Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.
Bonus Qualifications
Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.
Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods.
Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation.
Experience with learned world models, model-based reinforcement learning, or planning.
Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals.
Interest in translating fundamental research into reliable methods that can be evaluated on real robotic systems and practical downstream tasks.
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