Intern: Adaptive Embodied AI & Test-Time Learning

Honda Research Institute USA; San Jose (CA); On-site; USD 33,000 - 50,000; Full time

Job summary

Honda Research Institute USA invites a Research Intern to explore test-time adaptation for embodied agents. Based in San Jose, CA, the intern will study when to revise representations or gather more information, using deployment evidence to guide updates.

The role emphasizes multimodal models, interaction, and continual learning in embodied AI The position targets Ph.D. or MS students in CS/ML with experience in PyTorch and relevant learning paradigms, offering a 3-month internship starting Jan

Qualifications

• Currently enrolled as a Ph.D. or MS student in Computer Science, Machine Learning, or a related field.
• Research experience in embodied learning, imitation learning, reinforcement learning, meta learning, transfer learning, lifelong learning.
• Experience with open-source ML/AI frameworks such as PyTorch.

Responsibilities

• Design, implement, and evaluate adaptive embodied AI agents that operate under uncertainty at test time.
• Use deployment evidence to revise an agent's task representation, state/world model, policy, or memory.
• Develop multimodal models integrating vision, language, and action for embodied/interactive settings.
• Build or extend experiments in embodied-AI and robot-learning environments.
• Compare adaptation mechanisms like in-context updates, retrieval, memory revision, and fast weights.
• Run controlled experiments and iterate on model and system design based on results.
• Document methodologies and findings in technical reports and papers.
• Collaborate with cross-disciplinary team members and participate in design reviews.

Skills

Embodied learning

Imitation learning

Reinforcement learning

Meta learning

Transfer learning

Lifelong learning

PyTorch

Education

Ph.D. or MS student in Computer Science or Machine Learning

Tools

PyTorch

Job description

Honda Research Institute USA invites a Research Intern to explore test-time adaptation for embodied agents. Based in San Jose, CA, the intern will study when to revise representations or gather more information, using deployment evidence to guide updates.

The role emphasizes multimodal models, interaction, and continual learning in embodied AI The position targets Ph.D. or MS students in CS/ML with experience in PyTorch and relevant learning paradigms, offering a 3-month internship starting Jan