Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors

Vaios Papaspyros 1 Konstantinos Chatzilygeroudis 1 Vassilis Vassiliades 1 Jean-Baptiste Mouret 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : 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.
Keywords : robotics
Type de document :
Communication dans un congrès
Bayesian Optimization: Black-box Optimization and Beyond (workshop at NIPS), 2016, Barcelone, Spain. 〈https://bayesopt.github.io/〉
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Vaios Papaspyros, Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret. Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors. Bayesian Optimization: Black-box Optimization and Beyond (workshop at NIPS), 2016, Barcelone, Spain. 〈https://bayesopt.github.io/〉. 〈hal-01407757〉

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