Safeworld Raises $12M Seed Round to Develop AI Robot Safety Standards

Safeworld Launches with Significant Seed Funding

Safeworld, a new company focused on robot safety, has officially launched, announcing a seed funding round exceeding $12 million. The investment was led by Shine Capital and a16z Speedrun, with additional contributions from Box Group, Carnegie Mellon University Endowment, Innovation Endeavors, and SV Angel.

Addressing Generative AI Robot Safety

The company was co-founded by Dr. Ding Zhao, director of the Safe AI lab at Carnegie Mellon University, alongside veteran executive Kyle Wong and machine learning engineer Simo Rachidi. Safeworld's core mission is to tackle the safety challenges posed by generative AI models in robotics, particularly their unpredictable nature compared to traditional algorithms. The company aims to address both the probabilistic evaluation of risk and the public trust in these systems.

Simulation-Based Safety Evaluation

Safeworld specializes in evaluating robotic control systems through simulations. These simulations incorporate realistic human models to test how robots respond in various scenarios. This approach is similar to methods used in autonomous vehicle development, but adapted for the more unstructured environments where robots operate and the varying safety standards of different facilities.

Testing Unpredictable Human Interactions

The company creates digital versions of specific environments, such as factory blind corners, using modeling tools like Genesis or MuJoCo. Within these simulations, a robot's real software is tested against thousands of scenarios involving human models to determine safe operating parameters, such as stopping distances and human detection capabilities. The challenge lies in accounting for the unpredictable nature of human behavior.