Max Robotics — Global Service Robot Directory + US Certification

About

Grace Gao is a robotics and autonomous systems researcher whose work sits at the intersection of safe autonomy, multi-robot coordination, and navigation under uncertainty. Her research addresses one of the field's most pressing challenges: ensuring that intelligent robotic systems operate reliably and safely in real-world, uncertain environments where traditional guarantees break down. Gao's most influential contribution, "Safe Reinforcement Learning Using Black-Box Reachability Analysis" (2022, 28 citations), tackles the critical gap between capable deep reinforcement learning controllers and the safety guarantees required for real-world deployment. By applying reachability analysis as a safety layer, her work provides a pathway toward certifiably safe robot motion planning even when system models are unknown. This thread extends across her portfolio, including neural network training with embedded safety constraints and safeguarding learning-based planners against motion and sensing uncertainties. Beyond individual robot safety, Gao investigates collective and multi-robot intelligence. Her bio-inspired Shinerbot platform drew on the emergent navigation behavior of Golden Shiner fish to enable scalable swarm navigation without explicit path planning or inter-agent communication. She has also contributed to decentralized connectivity maintenance and GNSS fault detection for autonomous vehicles in GPS-challenging urban environments. Collectively accumulating over 75 citations, her work represents a rigorous and creative effort to bridge theoretical safety frameworks with practical robotics systems.