Robotics Research Intern - Post-Training
Toyota Research Institute Los Altos, CA 12 weeks
What they're looking for
- This is a paid 12-week internship opportunity and is a hybrid, in-office role.
- Here’s a glimpse into the Internship experience from some of our TRI interns!
- Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
- Interest in open research problems involving large-scale machine learning grounded in physical systems.
- Proficiency in Python and a deep-learning framework such as PyTorch.
The full posting
Published by Toyota Research Institute. Original posting
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 advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
Qualifications
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Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
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Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
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Interest in open research problems involving large-scale machine learning grounded in physical systems.
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Proficiency in Python and a deep-learning framework such as PyTorch.
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Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.
Bonus Qualifications
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Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.
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Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods.
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Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation.
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Experience with learned world models, model-based reinforcement learning, or planning.
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Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals.
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Interest in translating fundamental research into reliable methods that can be evaluated on real robotic systems and practical downstream tasks.
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