Technique of Recovery Process and Application of AI in Error Recovery Using Task Stratification and Error Classification

Abstract

We have proposed an error recovery method using the concepts of task stratification and error classification. In this paper, the recovery process after the judgment of error is described in detail. In particular, we explain how to change the parameters of planning, modeling, and sensing when error recovery is performed. Furthermore, we apply artificial intelligence (AI) techniques, such as deep learning, to error recovery.

Cost-oriented Planning for Error Recovery in an Automation Plant

“…In the actual environment, unlike the ideal case, various factors can cause errors in machine performance. This section describes an error classification concept and our error recovery technique [7][8][9].…”

“…Next, suitable correction of the system is performed based on the tentative cause. The process returns to the previous step, and the task is executed again from this step (Figure 3) [7][8][9]. The same error is less likely to occur because the corrected process has been executed.…”

“…In this study, we investigate returning to a previous step for error recovery. We have considered methods of error recovery using task stratification and error classification [7][8][9]. In particular, we have shown techniques of plant correction which are performed using an error cause derived by the proposed method, and a method of planning for error recovery in consideration of cost has been proposed.…”

Evaluation Standard of Error Recovery Planning Focused on Revival Process from Failures in Robotic Manufacturing Plants

“…Over several years, our research [6], [7], [8], [9], [10] has focused on systematizing error recovery theory, resulting in a method based on task stratification and error classification concepts. The primary components of the robot system include sensing, modeling, planning, and execution sequences (Fig.…”

Predictive Learning of Error Recovery with a Sensorized Passivity‐Based Soft Anthropomorphic Hand

“…For grasp adaptation, there has been promising investigation into detecting "anomalies" during manipulations, [34,43] training error detection using sequential neural networks with visual inputs, [44] and frameworks for error recovery. [20,45] Though there is notable lack of investigation in this area with soft hands, slip detection and prevention is one of the most common grasp adaptation strategies. [46][47][48][49][50] However, failure due to slip is just one of the many failure modes that occur after grasping and requires dense tactile information and high control bandwidth for adaptation.…”