Robotics Intern, Dexterous Manipulation – Intuitive Physics

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 Human-Centered AI, Human Interactive Driving, Energy and Materials, Machine Learning, and Robotics.

This is a Summer 2023 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

The Mission

We conduct research aimed at creating general-purpose robots that can scale to complex tasks in diverse environments. Our belief is that these robots must increase in capability and behavioral complexity over time without requiring impractical amounts of human expertise and engineering. To accomplish this, we want to enable robots to quickly acquire new skills with minimum human effort. We currently focus on learning from demonstrations as well as controller synthesis from learned dynamics.

The Team

The Dexterous Manipulation Team’s charter is to push the frontiers of research in robotics and machine learning to develop the future capabilities required for general-purpose robots able to operate in unstructured environments such as homes. We currently focus more on interaction and manipulation than we do on locomotion and much of our work currently takes place on bimanual robot manipulation stations. A long-running goal of our group is to lay the research groundwork for general-purpose in-home robots that can assist humans with tasks such as preparing food, keeping their house clean, or doing laundry.

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.
• We’re particularly interested in representation learning that combines non-visual modalities including tactile sensors and camera images.
• Self supervised state and / or task representation learning from large datasets for robotic manipulation.
• Quick and efficient improvement of learned policies.
• Controller synthesis with learned dynamics for complex physical tasks.
• Learning to detect, and react to, uncertainty and distribution shift in learned representations and policies.

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.

Qualifications

• Hands-on experience with RL, offline RL or behavior cloning for manipulation in simulation. Hardware experience not required, but strongly preferred.
• Experience with machine learning and familiarity with a major deep-learning framework.
• Familiarity with computer vision, particularly visual scene/object embeddings.
• Strong software development skills in Python (C++ is also useful).
• A passion for robotics and doing research grounded in important fundamental problems.

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