Research Intern: Test-Time Adaptation for Embodied Agents

Honda Research Institute USA; San Jose (CA); On-site; USD 28,000 - 47,000; Full time

Job summary

Honda Research Institute USA in San Jose, CA is offering a Research Intern position focused on test-time adaptation for embodied AI. The role involves investigating how deployment evidence guides when to revise models and when to rely on existing ones, with emphasis on interaction-conditioned adaptation and human feedback.

The intern will design adaptive agents, explore multimodal models, and run controlled experiments, reporting methodologies and findings through technical documents.

Qualifications

• Currently enrolled as a Ph.D. or M.S. student in Computer Science or Machine Learning.
• Research experience in embodied learning, imitation learning, reinforcement learning, meta-learning, transfer learning, or lifelong learning.
• Experience with open-source ML/AI frameworks (PyTorch, etc.).

Responsibilities

• Design, implement, and evaluate adaptive embodied AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.
• Design and implement methods that use deployment evidence to revise an agent's task representation, state or world model, policy, memory, or another appropriate component in its decision-making apparatus.
• Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.
• Build or extend experimental tasks in embodied AI, robot learning, or related interactive simulation environments.
• Compare relevant adaptation mechanisms, such as in-context updates, retrieval, explicit memory revision, parameter-efficient optimization, fast weights, or world- and action-model adaptation.
• Run controlled experiments, analyze results, and iterate on model and system design based on empirical findings.
• Document methodologies and findings through technical reports, presentations, and research papers.
• Collaborate with cross-disciplinary team members and participate in research discussions and design reviews.

Skills

PyTorch

Machine Learning

Reinforcement Learning

Education

PhD or MS student in CS/ML

Tools

Python

PyTorch

Job description

Research Intern: Test-Time Adaptation for Embodied Agents - Honda Research Institute USA

Research Intern: Test-Time Adaptation for Embodied Agents

Job Number: P26INT-16

Honda Research Institute USA (HRI-US) is seeking a self-motivated research intern to investigate test-time adaptation for embodied AI. The intern will investigate how evidence gathered during deployment can determine what an agent should revise, when it should gather more information or preserve its current model, and how useful corrections can persist across future interactions. The research will emphasize interaction-conditioned adaptation by learning from observations, action outcomes, failures, corrections, and human feedback collected while an agent operates.

San Jose, CA

Key Responsibilities

• Design, implement, and evaluate adaptive embodied AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.
• Design and implement methods that use deployment evidence to revise an agent's task representation, state or world model, policy, memory, or another appropriate component in its decision-making apparatus.
• Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.
• Build or extend experimental tasks in embodied AI, robot learning, or related interactive simulation environments.
• Compare relevant adaptation mechanisms, such as in-context updates, retrieval, explicit memory revision, parameter-efficient optimization, fast weights, or world- and action-model adaptation.
• Run controlled experiments, analyze results, and iterate on model and system design based on empirical findings.
• Document methodologies and findings through technical reports, presentations, and research papers.
• Collaborate with cross-disciplinary team members and participate in research discussions and design reviews.

Minimum Qualifications

• Currently enrolled as a Ph.D. or M.S. student in Computer Science, Machine Learning, or a related field at a reputable university.
• Research experience with projects related to embodied learning, imitation learning, reinforcement learning, meta-learning, transfer learning, or lifelong learning.
• Experience with open-source ML/AI frameworks (PyTorch, etc.).

Bonus Qualifications

• Experience with embodied simulation platforms, video datasets, egocentric observations, or other data relevant to robot policy learning.
• Familiarity with post-training methods for multimodal AI models.
• Strong publication record on topics related to multi-agent systems in robotics or AI conferences (RSS, CoRL, NeurIPS, AAAI, AAMAS, ICLR).

Years of Work Experience Required 0

Desired Start Date

May 2027

Internship Duration

3 Months

Position Keywords

Robot Learning, Embodied AI, Multimodal Foundation Models

For California applicants: Please review the Honda California Job Applicant Privacy Notice before submitting your application.