Secured sole-source SBIR Phase III contract by Chief of Wargaming, Office of the Under Secretary of Defense for Acquisition & Sustainment, and US Air Force Autonomy Prime to scale large-scale multi-agent decision-support AI.
At Multi AI, we have built a multi-agent reinforcement learning platform for complex planning and coordination problems of large numbers of assets in the military.
Awarded SBIR Phase III contract by Air Force Autonomy Prime to advance multi-agent AI for large-scale vehicle planning and coordination. Spearheaded cutting-edge AI innovations to enhance autonomous system capabilities in defense applications.
Chosen for the prestigious Capital Factory Accelerator, the largest startup accelerator in Texas, providing exclusive mentorship, funding opportunities, and access to a vast network of investors and industry leaders.
From voice command to synchronized flip: we show our early autonomous mission planning work that grew into Multi AI, including Tello drone choreography and a loyal wingman simulation for unmanned-manned teaming.
Awarded six-figure SBIR Phase II contract by Air Force Agility Prime to advance multi-agent AI for large-scale vehicle planning and coordination. Spearheaded cutting-edge AI innovations to enhance autonomous system capabilities in defense applications.
This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through highly constrained environments. The recent Learning from …
We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether …
Inferring spatial-temporal properties from data is important for many complex systems, such as additive manufacturing systems, swarm robotic systems and biological networks. Such systems can often be modeled as a labeled graph where labels on the …