Multimodal Robot Policy Intern: Dexterous Manipulation

Honda Ri; San Jose (CA); On-site; USD 34,000 - 44,000; Full time

Job summary

Honda Research Institute USA in San Jose, CA, seeks a self-motivated research intern to advance robot policy learning for multi-fingered dexterous manipulation using multimodal sensory data. The internship will explore scalable data acquisition and multimodal representation to develop policies that generalize across tasks and environments.

You will work with real robot hardware and publish results when applicable.

Qualifications

• Ph.D. or highly qualified MS candidate in robotics, computer science, or a related field.
• Experience with representation learning using multimodal robot data (e.g., RGB, RGB-D, and tactile).
• Experience with policy deployment on real-world robots.
• Experience with robotic simulators (e.g., Isaac Sim or MuJoCo).
• Proficient with Python, C++, and PyTorch.
• Experience with the Robot Operating System (ROS 2)

Responsibilities

• Explore cost-effective and scalable data collection strategies for real robot hardware, complemented by simulation where beneficial.
• Develop and evaluate training approaches that leverage heterogeneous and multimodal data to improve policy generalization across tasks and environments.
• Iteratively investigate the interplay between data collection and model training, evaluating how data design choices affect downstream policy performance.
• Implement baselines and perform benchmark evaluations on hardware and in simulation where applicable.
• Document findings and contribute to internal research reports.
• Contribute to the portfolio of patents and publish research results when applicable at top-tier conferences and journals in robotics and machine learning.

Skills

Python

C++

PyTorch

ROS 2

Multimodal learning

Policy deployment

Education

Ph.D. or MS candidate in robotics or CS

Tools

Isaac Sim

MuJoCo

Job description

Honda Research Institute USA in San Jose, CA, seeks a self-motivated research intern to advance robot policy learning for multi-fingered dexterous manipulation using multimodal sensory data. The internship will explore scalable data acquisition and multimodal representation to develop policies that generalize across tasks and environments.

You will work with real robot hardware and publish results when applicable.