Physical AI Data Annotation & Evaluation

Introduction

A frontier research lab’s robotics team partnered with Appen to accelerate the development of their Physical AI systems for domestic household environments. As the lab’s robotics team scaled its efforts to train models capable of real-world manipulation tasks, they needed a data partner that could provide high-quality annotations and evaluations of egocentric human videos and robot performance.

Challenge

Developing Physical AI systems for domestic household environments presented several key challenges:

• Scarcity of real-world robotics data: Unlike language or vision models that can be trained on vast web-scraped datasets, real-world robotic manipulation data must be custom collected and annotated. Existing open-source datasets offer limited coverage of household tasks, making high-quality annotated egocentric video data a critical bottleneck for model improvement.
• Evaluating physical performance of robots is nuanced: Assessing robot performance requires going beyond simple pass/fail judgements. Evaluators need to be trained on how to make fine-grained qualitative assessments on subtle performance dimensions such as motion efficiency and grasp accuracy.

Solution

Leveraged Appen’s large global workforce to rapidly scale the project across multiple annotation and evaluation workflows:

• Annotation of egocentric human videos to create action-labelled datasets for training Physical AI. This involved segmenting videos into granular task intervals and labelling each with its timestamp, task type, hand configuration, and a natural language description.
• Evaluation of egocentric human videos against a grading rubric that covered failure modes such as incomplete task completion, unrealistic environments, and extraneous human participants.
• Evaluation of teleoperated robot performance by scoring task execution across key dimensions such as motion efficiency, grasp accuracy, and object manipulation.

Results

Appen has delivered 50,000+ units of data across the combined annotation and evaluation workflows so far, enabling the client to improve performance of their Physical AI systems in domestic household environments.

Conclusion

This partnership between Appen and the frontier research lab showcases how scalable, high-quality human annotation and evaluation can help unlock progress in Physical AI. By combining Appen’s global workforce with structured workflows purpose-built for Physical AI, the collaboration has delivered granular video annotations and nuanced robot performance evaluations needed to drive meaningful improvement.

At Appen, we are committed to partnering with teams at the frontier of Physical AI. Explore how we can help advance your Physical AI systems by contacting us below.