Using perceptual information to predict task outcome
Abstract Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Speciﬁcally, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workﬂow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and speciﬁcity is similar with and without the proposed mechanism, while achieving a signiﬁcant data size reduction due to the periodic approach taken.
In order to properly take advantage of the information stored into the ASM, we propose a system that monitors continuously the execution of a motor skill (in this case biped locomotion) and looks for deviations that could evolve into movement failures. The failure detection protocol we introduce in this work was inspired by Pastor et al. , but utilizes a more refined statistical analysis in order to achieve the best results possible.
J. Andre, C. Santos, L. Costa
Department of Production and System, University of Minho
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