Behavioral Learning of Dish Rinsing and Scrubbing based on Interruptive Direct Teaching Considering Assistance Rate

TL;DR

This paper presents a robot learning system for safe and dexterous dishwashing tasks, using interruptive direct teaching to estimate assistance needs and adapt control strategies for unknown objects.

Contribution

It introduces a novel learning framework that models object dynamics and assistance rate to enable safe, adaptive dishwashing manipulation without excessive human help.

Findings

The robot successfully learned to rinse and scrub dishes with minimal assistance.

The system effectively estimated assistance rate to improve safety and dexterity.

Adaptive control reduced the need for human intervention during dishwashing tasks.

Abstract

Robots are expected to manipulate objects in a safe and dexterous way. For example, washing dishes is a dexterous operation that involves scrubbing the dishes with a sponge and rinsing them with water. It is necessary to learn it safely without splashing water and without dropping the dishes. In this study, we propose a safe and dexterous manipulation system. The robot learns a dynamics model of the object by estimating the state of the object and the robot itself, the control input, and the amount of human assistance required (assistance rate) after the human corrects the initial trajectory of the robot's hands by interruptive direct teaching. By backpropagating the error between the estimated and the reference value using the acquired dynamics model, the robot can generate a control input that approaches the reference value, for example, so that human assistance is not required and the dish does not move excessively. This allows for adaptive rinsing and scrubbing of dishes with unknown shapes and properties. As a result, it is possible to generate safe actions that require less human assistance.