Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
This work addresses safety concerns in robot damage recovery, which is an incremental improvement for robotics applications.
The paper tackled the problem of robots adapting to damage without safety constraints by introducing a safety-aware algorithm that uses constrained Bayesian optimization and simulated priors, resulting in improved crawling speed within safe regions and fewer unsafe trials compared to existing methods.
The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E leads to fast damage recovery, it does not incorporate any safety constraints that prevent the robot from attempting harmful behaviors. In this work, we address this limitation by replacing the BO component with a constrained BO procedure. We evaluate our approach on a simulated damaged humanoid robot that needs to crawl as fast as possible, while performing as few unsafe trials as possible. We compare our new "safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives. Our results show that our algorithm outperforms the other approaches, both in crawling speed within the safe regions and number of unsafe trials.