Incremental Learning for Robot Sensing and Control

The purpose of this proposal is to build a working prototype of a highly-adaptive, vehicle-independent, compact, low-power, low-cost, autonomous ground robot navigation system that incorporates the results obtained in our Phase I effort and in our earlier DARPA LAGR (Learning Applied to Ground Robots) work. The system will be able to quickly and automatically adapt to changing environments in real time. Near-to-far learning techniques provide sensing far beyond stereo and LIDAR range, and deep learning techniques allow terrain classification, people detection, and the ability to automatically learn from the robot's own experience and from observations of human drivers (in semi-autonomous mode). We will show the prototype's readiness for commercial use by demonstrating its capabilities on at least two different vehicle platforms in realistic outdoor settings using military-relevant use cases. The system will be independent of any particular robot platform and will be capable of operating both self-sufficiently, relying only on its built-in sensors, or in an integrated unit with existing on-board sensors. Our system is designed to fully operate with passive vision-based sensors alone but its performance can be enhanced with additional sensor input, if available, such as LIDAR.