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Task Adaptivity in Autonomous Agents
Job Number: P23INT-19
We are seeking a motivated and innovative research intern to work on a project involving robots learning to adapt to new tasks with minimal prior experience. This includes developing and implementing state-of-the-art approaches to enhance adaptability and versatility of robotic systems in the real world.
Ann, Arbor, MI
| Duration |
3 Months |
| Position Introduction |
We are seeking a motivated and innovative research intern to work on a project involving robots learning to adapt to new tasks with minimal prior experience. This includes developing and implementing state-of-the-art approaches to enhance adaptability and versatility of robotic systems in the real world.
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| Key Responsibilities |
During the time of the internship, you are expected to:
- Explore and develop novel algorithms and techniques that enable robots to adapt and learn new tasks autonomously
- Implement one or a combination of algorithms involving reinforcement learning, meta-learning, transfer learning, and other similar approaches in simulation
- Collaborate with a multidisciplinary team of researchers to design and implement cutting-edge solutions.
- Publish research findings in top-tier conferences and journals
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| Minimum Qualifications |
- Ph.D. candidate in mechanical engineering, electrical engineering, computer science, or similar fields
- Strong publication record
- Previous experience in research or projects related to robotics, AI, meta learning, transfer learning, multi-task learning or lifelong learning
- Excellent programming skills in Python, C++, and experience with popular machine learning frameworks (TensorFlow, PyTorch)
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| Bonus Qualifications |
- Passion for learning new software tools and languages.
- Proficiency in reinforcement algorithms for multi-agent systems
- Strong problem-solving skills
- Excellent communication skills
- Be able to work independently within a multicultural environment
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| Years of Work Experience Required: |
2~5 years |
| Position Keywords |
Machine learning, reinforcement learning, meta learning, multi-task learning, transfer learning
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Alternate Way to Apply
Send an e-mail to careers@honda-ri.com with the following:
- Subject line including the job number(s) you are applying for
- Recent CV
- A cover letter highlighting relevant background (Optional)
Please, do not contact our office to inquiry about your application status.
