Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Cornhole, traditionally seen as tailgate entertainment, has rapidly risen in popularity since the launching of the American Cornhole League in 2016. However, it lacks robust quality control over large tournaments, since many of the matches are scored and refereed by the players themselves. In the past, there have been issues where entire competition brackets have had to be scrapped and replayed because scores were not handled correctly. The sport is in need of a supplementary scoring solution that can provide quality control and accuracy over large matches where there aren’t enough referees present to score games. Drawing from the ACL regulations as well as personal experience and testimony from ACL Pro players, a list of requirements was generated for a potential automatic scoring system. Then, a market analysis of existing scoring solutions was done, and it found that there are no solutions on the market that can automatically score a cornhole game. Using the problem requirements and previous attempts to solve the scoring problem, a list of concepts was generated and evaluated against each other to determine which scoring system design should be developed. After determining that the chosen concept was the best way to approach the problem, the problem requirements and cornhole rules were further refined into a set of physical assumptions and constraints about the game itself. This informed the choice, structure, and implementation of the algorithms that score the bags. The prototype concept was tested on their own, and areas of improvement were found. Lastly, based on the results of the tests and what was learned from the engineering process, a roadmap was set out for the future development of the automatic scoring system into a full, market-ready product.