Research Intern: Scaling Multimodal Robot Policy Learning for Dexterous Manipulation - Honda Research Institute USA

Research Intern: Scaling Multimodal Robot Policy Learning for Dexterous Manipulation

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Research Intern: Scaling Multimodal Robot Policy Learning for Dexterous Manipulation

Job Number: P26INT-23
Honda Research Institute USA (HRI-US) is seeking a self-motivated research intern to advance robot policy learning for multi-fingered dexterous manipulation using multimodal sensory data. The internship will explore generalizable policy learning from a data-scaling perspective, including scalable data acquisition and multimodal representation and policy learning. The goal is to develop dexterous manipulation policies that generalize across diverse tasks and environments, including precision-demanding and contact-rich tasks. The successful candidate will work with real robot hardware to implement, evaluate, and iteratively refine the proposed approach.
San Jose, CA

 

Key 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.

 

Minimum Qualifications

 
  • Ph.D. or highly qualified M.S. 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)

 

Bonus Qualifications​

  • Experience with in-hand dexterous manipulation on multi-fingered robot hands.
  • Experience with scalable or low-cost data collection for robot learning (e.g., teleoperation systems, human demonstration capture, or wearable and handheld devices).
  • Experience training robot policies on large-scale or heterogeneous datasets (e.g., human videos, cross-embodiment data, or mixed real and simulated data).
  • Experience with multimodal sensing for manipulation, particularly tactile and force sensing.
  • Experience with large vision-language-action (VLA) models or other robot foundation models.
  • Experience with sim-to-real approaches.

 

Years of Work Experience Required   0
Desired Start Date  May 2027
Internship Duration  3 Months
Position Keywords  

​Dexterous manipulation, Embodied AI, Robot Foundation models

Alternate Way to Apply

Send an e-mail to careers@honda-ri.com with the following:
- Subject line including the job number(s) you are applying for 
- Recent CV 
- A cover letter highlighting relevant background (Optional)

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