Robotics Research Intern - Post-Training

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 Large Behavior Models, Robotics, Energy & Materials, Human-Centered AI, and Human-Interactive Driving.
This is a paid internship opportunity. Please note that this internship will be an in-office role in Cambridge, MA.
The Vision
We envision a future with robots that help people in our real environments, with interactions that allow robots to understand, adapt, and grow with us. We believe that robots should and will work well alongside people: helping where wanted, and ultimately enabling 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. We must also create robots that people can understand, collaborate with, and rely on.
The Team
Our goal is to revolutionize the field of robotics, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real-world environments with people.
Our team has deep cross-functional expertise across hardware, simulation, perception, controls, and machine learning. We measure our success in terms of fundamental capabilities development, as well as research impact via open-source software and publications. Come join us and let’s make general-purpose robots a reality.

Some of our ongoing work is highlighted here.
The Internship
We have several research thrusts under our broad mission, and we are looking for a research intern in any of these areas:
Data-efficient and general algorithms for learning robust policies leveraging multiple sensing modalities: proprioception, images, force, and dense tactile sensing.
Scaling learning approaches to large-scale models trained on diverse sources of data including web-scale text, images, and video.
Quick and efficient improvement of learned policies.
Developing and deploying learned policies and complex mobile manipulator embodiments, such as humanoid robots.
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 and dynamic corpus of robot data.
Areas of focus
Vision-Language-Action (VLA) models for mobile manipulation 
Dynamic whole-body manipulation on humanoids
Cross-embodiment transfer
Universal (UMI-style or ego-centric) data collection methods
Haptic/tactile-inclusive VLA models
Integration of VLA policy methods with model-based robotics methods
Large-scale synthetic data generation and sim-to-real transfer
Post-training for continual learning

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.

The pay range for this position at the 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, business or organizational needs, market location, job-related knowledge, skills, and experience. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.
 
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