Synthetic Data Pipelines for Adaptive, Mission-Ready Militarized Humanoids

TL;DR

This paper introduces a synthetic data pipeline that accelerates training and validation of militarized humanoids by generating high-fidelity, adaptable simulated scenarios for improved perception, navigation, and decision-making in complex environments.

Contribution

It presents a novel synthetic data generation approach that enables rapid, scalable, and adaptable training for militarized humanoids across diverse operational scenarios.

Findings

Enables quick tuning of datasets for new environments and threats

Supports development of advanced subsystems like multimodal sensing and survivability

Reduces reliance on costly and risky field trials

Abstract

Omnia presents a synthetic data driven pipeline to accelerate the training, validation, and deployment readiness of militarized humanoids. The approach converts first-person spatial observations captured from point-of-view recordings, smart glasses, augmented reality headsets, and spatial browsing workflows into scalable, mission-specific synthetic datasets for humanoid autonomy. By generating large volumes of high-fidelity simulated scenarios and pairing them with automated labeling and model training, the pipeline enables rapid iteration on perception, navigation, and decision-making capabilities without the cost, risk, or time constraints of extensive field trials. The resulting datasets can be tuned quickly for new operational environments and threat conditions, supporting both baseline humanoid performance and advanced subsystems such as multimodal sensing, counter-detection survivability, and CBRNE-relevant reconnaissance behaviors. This work targets faster development cycles and improved robustness in complex, contested settings by exposing humanoid systems to broad scenario diversity early in the development process.