Inception Robotics, LLC

Inception Robotics, LLC is a self-certified small disadvantaged business registered to receive federal contract and grant awards. The company specializes in developing autonomous mobile robots (AMRs) and innovative robotic technologies for various applications. Inception Robotics focuses on research and development in areas such as collision avoidance, deep reinforcement learning, and socially-compliant navigation for robots operating in densely crowded spaces. As a limited liability company and for-profit organization, Inception Robotics has been actively engaged in federal contracting and grant programs since its registration in SAM.gov on May 20, 2021.

Through federal contract awards, Inception Robotics has primarily worked with the Department of the Army on Small Business Innovation Research (SBIR) projects. The company has secured both Phase I and Phase II SBIR contracts, demonstrating its ability to progress from initial concept development to more advanced research and prototype creation. In August 2023, Inception Robotics was awarded a $149,719 Phase I SBIR contract to establish the technical merit, feasibility, and commercial potential of proposed innovations. More recently, in May 2024, the company received a larger Phase II SBIR contract with a ceiling value of $1,897,334.99, set to be completed by November 9, 2025. These contracts, designated as total small business set-asides, reflect the Department of the Army's commitment to fostering technological advancements through small business participation in the defense industry.

On the grant front, Inception Robotics has been awarded funding from the National Science Foundation (NSF) through its SBIR program. In March 2022, the company received a project grant focused on developing dense, socially-compliant autonomous delivery robots. This grant, scheduled to conclude on February 29, 2024, supports research into a hybrid collision avoidance approach that enables AMRs to operate safely in dense crowds while maintaining social compliance in sparse scenarios. The project aims to address challenges such as the sim-to-real gap, performance optimization for embedded processors, and improved localization in crowded environments. This NSF-funded research has potential applications in various industries, including airports, retail, healthcare, and hospitality, where AMRs could provide contactless deliveries and navigate complex indoor environments without relying on GPS.