Postdoctoral Researcher, Human Aware Interaction Learning at Toyota Research Institute in Cambridge, MA

Job description

• Our team at Toyota Research Institute is looking for a motivated and capable postdoc to conduct research on the computational and machine learning aspects of human-robot interaction. Areas of interest include applying foundation models to human-robot interaction, with an emphasis on understanding human-robot collaboration and how it evolves as foundation models become increasingly central to robotic manipulation.
• We’re looking for a postdoc with a solution driven attitude who can partner closely with our research team and turn ideas into impactful results. Relevant background topics include robot foundation models (e.g., VLAs, WAMs), imitation/reinforcement learning, leveraging language conditioning in trajectory planning/prediction, and decision-making under uncertainty, as well as human-machine teaming/shared control.
• In this project, we’re exploring novel approaches to efficiently learn how humans and robots collaborate. Our overall goal is to create AI approaches that challenge more traditional approaches to novel interactions with a human collaborator and enable the development of new embodied interactive policies.
• The project will be focused on researching a new approach in a specific sub-area of the overall goal, towards publications in top-tier conferences, well-matched for a postdoc that is looking for an additional year of research before beginning a faculty position, or a more experienced existing faculty member looking to take a year-long sabbatical with us. The fellowship will be in our Cambridge, MA research center, where you will be working with researchers on meaningful research problems, with access to the resources needed to pursue ambitious projects and publications. Applicants with relevant publications in these fields are highly encouraged to apply.

Responsibilities

• Perform research and publish on relevant topics in appropriate venues. Depending on the exact project outcome, publication target venues include, e.g., ICRA, CVPR, NeurIPS, and HRI. Publication emphasis is on how novel representations allow robots to learn to interact with humans towards long-term shared autonomy in a data-efficient, robust, and explainable way, with results in our teams’ problems and domain of interest.
• Exploration of both computational and cognitive phenomena, working with a team of researchers to create new approaches for understanding, predicting, and interacting with humans.
• Work from approach inception and ideation to validation of the developed approaches.

Requirements

• Ph.D in related fields (by end date) - ML/AI, robotics, human-centric AI, or computer vision. Some experience with robot systems is needed.
• Strong publications record in top ML/robotics venues. Specific areas: robot foundation models and their use for human interaction, ML and synthetic data for human-collaborative embodied reasoning, imitation/reinforcement learning, and learning-based shared control and planning for human interactions.
• Coding: Python, experience working in a team on joint scientific projects. Experience training deep learning models in e.g., Pytorch/TensorFlow.
• An ideal candidate can refine a topic and execute a scientific research plan in collaboration with other researchers.

Skills

Required

• Python
• Some experience with robot systems

Preferred

• Imitation/reinforcement learning
• Decision-making under uncertainty
• Human-machine teaming/shared control

Benefits

• The pay range for this position at commencement of employment is expected to be between $136,800and $196,650/year for Massachusetts-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, 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.