Research Intern: Adaptivity Autonomous Agents

Research Intern:
Adaptivity For Autonomous Agents - Honda Research Institute USA

Honda Research Institute USA (HRI-US) is seeking a self-motivated intern to work on foundational research focusing at the intersection of test-time adaptivity for AI agents and human-AI collaboration. The intern will work on building and evaluating adaptive, agentic AI systems that can reason, learn, and adjust their behavior in response to changing environments and human feedback. The project explores how multimodal AI agents operating can collaborate with humans more effectively through adaptive inference, interactive decision-making, and goal-directed behavior.

This role is ideal for a student interested in modern AI research spanning foundation models, embodied AI, reasoning systems, and human-centered AI.

• Design, implement, and evaluate adaptive AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.
• Conduct research on human–AI collaboration, including interaction protocols, shared autonomy, and mechanisms for incorporating human guidance into agent decision-making.
• Work with simulation environments (e.g., Virtual Home, Habitat, or related embodied AI platforms) to build tasks, scenarios, and evaluation frameworks.
• Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.
• Investigate reasoning and agentic behaviors, such as planning, decomposition, memory, tool use, or self-reflection in adaptive systems.
• 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.

• Currently enrolled as a M.S. or a Ph.D. student in Computer Science, Machine Learning, or a related field at a reputed university.
• Research experience in projects related to reinforcement learning, meta learning, transfer learning, lifelong learning.
• Experience in open-source deep learning frameworks.

• Experience with multi-agents settings (simulation testbeds, etc).
• Familiarity with post-training multimodal AI models.
• Strong publication records in topics related to multi-agent systems in robotics or AI conferences (RSS, CORL, NeurIPS, AAAI, AAMAS, ICLR).