Key Responsibilities

• Research, design, adapt and apply multimodal foundation models (e.g., VLMs, VLAs) for robotic systems to develop robust manipulation behaviors and task failure reasoning.
• Train, fine-tune, and design vision-language and multimodal foundation models for robotic dexterous manipulation.
• Research and develop machine learning algorithms such as reinforcement learning and imitation learning for contact-rich dexterous manipulation.
• Implement and validate algorithms in simulation and on robot hardware.
• Analyze system performance and improve robustness and generalization.
• Deliver results in accordance with project timelines.
• Prepare written and oral technical reports and demonstrations.
• Contribute to portfolio of patents, academic publications, and prototypes to demonstrate research value.
• Collaborate with our teams of scientists and engineers in Honda’s regional and global R&D offices, as well as with our partners in academia and industry.
• Supervise interns.

Minimum Qualifications

• Ph.D. in robotics, mechanical engineering, electrical engineering, computer science, or a related field
• Expert knowledge on learning-based methods such as reinforcement learning, generative models, and inference.
• Hands-on experience applying VLMs, VLAs, or multimodal foundation models in robotics or embodied settings.
• Experience with model training, fine-tuning, or architectural development of multimodal/foundation models.
• Familiarity with transformer-based and multimodal models.
• Solid knowledge of fundamentals of robotics, including kinematics, dynamics, control systems.
• Experience with planning, control, and system integration for robotic manipulation.
• Strong programming skills in Python or C++ and familiarity with ROS.
• Self-motivated to advance the project and deliver on time.
• Strong track record of publications in robotics and machine learning venues.
• Strong written and oral communication skills.
• 1+ years of relevant work experience.

Bonus Qualifications