Abstract

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require antici-pating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience al-gorithm, a new algorithm that allows robots to quickly and au-tonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algo-rithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evalu-ate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we com-pare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches. 1.