Robotics Intern, World Action Models for Robot Policy

Toyota Research Institute•Cambridge, MA

3d•$40 - $58•Hybrid

About The Position

Toyota Research Institute is seeking a research intern to adapt pre-trained world foundation models into world action models that serve as robot policies. The work may use mid-training, fine-tuning, or post-training techniques, with policies intended for deployment on humanoid platforms or other mobile manipulators in human-centered environments. 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 research may focus on model architecture and training, long-horizon task execution, dynamic human-robot interaction, or real-world physical robot evaluation. The role involves working with large existing datasets and a growing corpus of robot and human-interaction data. This is a paid, 12-week internship and a hybrid, in-office role. The expected starting pay range is $40–$58 per hour for Massachusetts-based roles. Pay depends on factors including experience, skills, job-related knowledge, and market location.

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, such as 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

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 ML tooling, simulation, robot middleware, and data pipelines.
C++ experience is helpful but not required.

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.