Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models

TL;DR

This paper introduces methods for diagnosing execution failures in robots using learned constraints and generating synthetic successful experiences to improve robot action models, demonstrated on handle grasping tasks.

Contribution

It presents a novel approach combining failure diagnosis with synthetic data generation to enhance robot execution models, specifically for parameterised actions.

Findings

Diagnosis algorithm effectively identifies violating parameters.

Synthetic data improves success rate of robot actions.

Corrected experiences enhance learning of execution models.

Abstract

When faced with an execution failure, an intelligent robot should be able to identify the likely reasons for the failure and adapt its execution policy accordingly. This paper addresses the question of how to utilise knowledge about the execution process, expressed in terms of learned constraints, in order to direct the diagnosis and experience acquisition process. In particular, we present two methods for creating a synergy between failure diagnosis and execution model learning. We first propose a method for diagnosing execution failures of parameterised action execution models, which searches for action parameters that violate a learned precondition model. We then develop a strategy that uses the results of the diagnosis process for generating synthetic data that are more likely to lead to successful execution, thereby increasing the set of available experiences to learn from. The diagnosis and experience correction methods are evaluated for the problem of handle grasping, such that we experimentally demonstrate the effectiveness of the diagnosis algorithm and show that corrected failed experiences can contribute towards improving the execution success of a robot.