VeriDream

Robots act in the real world. When deploying AI methods on robots, the continuous and dynamic nature of the physical world raises many challenges which are not encountered in purely digital domains such as Internet search and social networks. To address these challenges, VeriDream builds on the DREAM and RobDream research projects to pursue a two-fold innovation strategy for AI in robotics. Its deep innovation strategy will strive to achieve high technological readiness in a set of use cases at a warehouse logistics startup. Its broad innovation strategy will promote a wider uptake of effective innovation methods in SMEs, thus enhancing the innovation potential of SMEs in AI for robotics.

VeriDream members GoodAI and ISIR (Sorbonne University) have been working on a case study that applies quality diversity algorithms to control robotic legs. Robotic leg control is a standard engineering task. We’re interested in a rapid adaptation to an arbitrary leg shape/number of joints. Quality diversity algorithms offer themselves as an efficient approach to discovering control policies and robot joint configurations that achieve a diverse set of goals and target configurations, respectively. Since these algorithms are inherently easy to parallelize, we can speed up the whole process by running multiple instances of the algorithm at the same time. The VeriDream consortium is using the videogame Space Engineers as a testing ground to run these algorithms. Space Engineers were a natural fit for the study, since its developer, Keen Software House, is GoodAI’s sister company.