Human Aware Interactions and Learning Intern

Toyota Research Institute•Cambridge, MA

2d•Hybrid

About The Position

Toyota Research Institute is seeking a paid research intern for its Human Aware Interaction and Learning (HAIL) team. The internship is 12 weeks and is a hybrid, in-office role. The research focuses on robot learning and human-robot interaction, including general-purpose machine-learning models that generate robot actions from sensor data and human interactions. Work may address data-efficient multimodal policies that reason about people and shared tasks; hierarchical reasoning over human intent, cues, and roles; adapting data-driven robot policies to human presence; uncertainty-aware teaming; using human-human and synthetic data; and shared autonomy with vision-language-action models. The intern is expected to develop working code prototypes, collaborate frequently with team members, run experiments using simulated and physical robots, and participate in publishing research at peer-reviewed venues. The intern will also work with existing large static datasets and help build and maintain a new corpus of robot data.

Requirements

Currently pursuing a Ph.D. or M.S. in Robotics, Computer Science, Mechanical Engineering, or a related field.
Publication record at top-tier robotics or machine-learning conferences such as RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, ICCV, or HRI.
Experience with machine learning and familiarity with large datasets, models, and benchmarks.
Strong software development skills in Python.
A make-it-happen attitude and comfort with fast prototyping and running informative experiments.
Passion for robotics helping people and having real-world, large-scale impact.

Nice To Haves

Hardware experience, especially deploying learned policies on real robotic systems; strongly preferred.
Experience in C++; helpful but not strictly required.
Experience conducting user studies involving real robot hardware.
Hands-on experience 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 using human feedback or corrections.
Experience with qualitative and quantitative user research, including developing study materials, IRB applications, facilitation and interviewing, managing personally identifiable information, and statistical analysis.
System integration skills, including use of state-of-the-art machine-learning tools and databases.

Responsibilities

Develop working code prototypes for research in robot learning and human-robot interaction.
Run experiments with simulated and physical robots.
Interact and collaborate frequently with team members.
Participate in publishing research in peer-reviewed venues.
Work with existing large static datasets and help build and maintain a new corpus of robot data.
Conduct research in one or more areas such as multimodal robot policies, hierarchical reasoning about human and shared task states, adapting policies to human presence, uncertainty-aware teaming, collaborative demonstration data, or shared autonomy.