Robotics Intern, World Action Models for Robot Policy

Toyota Research Institute•Cambridge, MA

1d•Hybrid

About The Position

Toyota Research Institute (TRI) is hiring a research intern to adapt pre-trained world foundation models—through mid-training, fine-tuning, or post-training—into world action models that serve as robot policies. These policies will be deployed on humanoid platforms or other mobile manipulators in human-centered environments. The project may focus on model architecture and training, long-horizon task execution, dynamic human-robot interaction, or real-world robot evaluation, depending on the intern’s research interests. The intern will work closely with researchers and engineers to develop prototypes, run experiments in simulation and on real robots, and publish results at peer-reviewed venues. The work involves large existing datasets and a growing corpus of robot and human-interaction data. The broader team develops Vision-Language-Action Models and world action models to help robots complete varied, multi-step tasks reliably, safely, and naturally alongside people.

Requirements

Currently pursuing a Ph.D. or M.S. in Robotics, Computer Science, Machine Learning, Mechanical Engineering, or a related field.
Experience training or fine-tuning large models (e.g., VLMs, VLAs, or video/world models) on large datasets.
Strong Python skills and fluency with a deep learning framework such as PyTorch.
A “make it happen” attitude and comfort with fast prototyping and informative experiments.

Nice To Haves

C++ experience.
Publications at top-tier robotics or machine-learning venues, such as RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or HRI.
Experience deploying learned policies on real robots, especially humanoids or mobile manipulators.
Background in world models, video prediction, or other model-based planning.
Experience with human-robot interaction, human motion or intent prediction, or learning from egocentric human video.
Experience with long-horizon or hierarchical task execution.
System integration skills across machine-learning tooling, simulation, robot middleware, and data pipelines.

Responsibilities

Conduct original research on world action models for robot policy, from problem definition through experiments and analysis.
Mid-train, fine-tune, or post-train world action models, and evaluate them in simulation and on humanoids or mobile manipulators.
Design evaluations that measure long-horizon task success and the quality of interaction with people.
Collaborate with researchers and engineers across teams, the Robotics division, and university partners.
Share results through internal presentations, peer-reviewed publications, and open-source releases where appropriate.