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.
Basic 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.
Preferred qualifications
- Hands-on experience on design, development, and fine-tuning of foundation models for dexterous or multi-fingered manipulation.
- Hands-on experience with policy deployment and evaluation on real robot stacks.
Tags & focus areas
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