Evolving Difficulty Targeted Bouldering Routes

The challenge of utilizing artificial intelligence to generate indoor rock climbing routes with a specific grade is an interesting and unsolved problem due to its complexity and subjectivity. We use MAP-Elites, an evolutionary, quality-diversity algorithm, in conjunction with GradeNet [8] to produce a set of disjoint MoonBoard climbing routes that sufficiently challenge a climber without exceeding their physical and technical limitations. We evaluate these routes through visual a assessment survey by climbers as well as an in-person study in which climbers attempt to climb the generated routes. While our algorithm generally performs well in producing complete or near-complete archives of diverse climbs at every difficulty level as assessed by GradeNet, they fall short when it comes to in person trials. Additionally, the data from user surveys, while supporting the claims of Duh and Chang [8] about GradeNet\u27s superiority to human grad- ing ability, is inconclusive in determining the success of our algorithm. These results leave open the path for future work to leverage the relative success of quality-diversity while accounting for the shortcomings of route quality and difficulty present in our system\u27s design

Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits

Soft robotics aims to develop robots able to adapt their behavior across a wide range of unstructured and unknown environments. A critical challenge of soft robotic control is that nonlinear dynamics often result in complex behaviors hard to model and predict. Typically behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. More recently, optimization algorithms such as Genetic Algorithms (GA) have been used to discover gaits, but these behaviors are often optimized for a single environment or terrain, and can be brittle to unplanned changes to terrain. In this paper we demonstrate how Quality Diversity Algorithms, which search of a range of high-performing behaviors, can produce repertoires of gaits that are robust to changing terrains. This robustness significantly out-performs that of gaits produced by a single objective optimization algorithm.Comment: 6 pages, submitted to IEEE RoboSof