ベイズノンパラメトリックVAR-HMMによるロボットの自己認識
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
ベイズノンパラメトリックなマルコフスイッチング過程を用いてロボットの接触タスクの複雑なダイナミクスをモデル化し、HMM系手法より汎化性能と計算効率に優れることをスナップ組立タスクで示した。
著者: Hongmin Wu, Hongbin Lin, Yisheng Guan, Kensuke Harada, Juan Rojas
分類: cs.RO
原文アブストラクト
Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.
