Research Intern: Human-Object Interaction Learning

Honda Research Institute USA; San Jose (CA); On-site; USD 34,000 - 69,000; Full time

Job summary

Honda Research Institute USA in San Jose, CA is seeking a motivated research intern to advance multimodal machine learning for human-object interaction understanding. You will develop representation learning methods using egocentric video, hand pose, and language to model hand-object relationships and manipulation dynamics.

The internship emphasizes rigorous experimentation and collaboration to produce high-impact results suitable for publication at top conferences.

Qualifications

• Enrolled in PhD program in computer science, electrical engineering, robotics, or related field.
• Strong background in machine learning and deep learning with CV or multimodal focus.
• Proficiency in Python and PyTorch.
• Experience with empirical ML experiments, data preprocessing, and ablations.
• Excellent communication and research publication potential.

Responsibilities

• Conduct literature reviews and propose novel directions for H-O interaction representation learning.
• Design and implement ML methods for multimodal representations from egocentric video, hand pose, and language.
• Investigate spatial relations, hand-object contact, and temporal interactions.
• Develop pipelines for data processing, model training, and evaluation.
• Contribute to publications at leading conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR).
• Communicate results and collaborate with researchers to publish high-impact work.

Skills

Python

PyTorch

Machine learning

Deep learning

Computer vision

Multimodal learning

Representation learning

Video understanding

Vision-language models

Hand pose

Education

Ph.D. in CS/EE/Robotics or related

Tools

PyTorch

Python

Job description

Research Intern: Human-Object Interaction Learning - Honda Research Institute USA

Research Intern: Human-Object Interaction Learning

Job Number: P26INT-10

Honda Research Institute USA (HRI-US) is seeking a self-motivated and independent research intern to join our team researching multimodal machine learning for human-object interaction understanding. This role will focus on developing novel representation learning methods that capture how humans interact with and manipulate objects, leveraging egocentric video, hand pose, object-centric information, and language. The intern will investigate learning approaches that model hand-object relationships, manipulation dynamics, and temporal interactions to support downstream tasks such as action understanding, error detection, procedural activity understanding, and skill assessment. This research-oriented internship aims to develop novel methodologies and produce high-impact results suitable for publication at a top-tier conference.

San Jose, CA

Key Responsibilities

• Conduct literature reviews to identify relevant prior work, formulate research hypotheses, and propose novel technical directions in human-object interaction representation learning.
• Design and implement machine learning methods for learning representations of human-object interactions from egocentric video, hand pose, object-centric features, and language.
• Investigate multimodal representation learning approaches that capture spatial relationships, hand-object contact, manipulation dynamics, and temporal interactions.
• Explore methods for integrating human-object interaction representations into modern video understanding models and vision-language models.
• Develop and evaluate learned representations on downstream tasks such as action recognition, temporal action segmentation, error detection, procedural activity understanding, and skill assessment.
• Design rigorous experiments, evaluation protocols, and ablation studies to assess representation quality, generalization, and downstream task performance.
• Build data processing, model training, and evaluation pipelines for multimodal human-object interaction learning.
• Analyze experimental results, communicate research findings, and collaborate with researchers to develop novel methods suitable for publication at leading conferences such as CVPR, ICCV, ECCV, NeurIPS, and ICLR.

Minimum Qualifications

• Currently pursuing a Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field.
• Strong background in machine learning and deep learning, with hands-on experience in computer vision, representation learning, or multimodal learning.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Demonstrated understanding of one or more of the following areas: video understanding, human-object interaction, action recognition, human pose modeling, representation learning, or vision-language models.
• Experience designing and conducting empirical machine learning experiments, including data preprocessing, model training, evaluation, and ablation studies.
• Strong analytical and problem-solving skills, with the ability to understand, implement, and extend methods from recent research papers.
• Strong written and verbal communication skills, with an interest in conducting original research and publishing at leading academic conferences.

Bonus Qualifications

• N/A

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

• Human-Object Interaction, Egocentric Video Understanding,
• Multimodal Learning, Representation Learning, Object Manipulation,
• Vision-Language Models