COFFAIL: A Dataset of Successful and Anomalous Robot Skill Executions in the Context of Coffee Preparation

TL;DR

COFFAIL is a new dataset capturing successful and anomalous robot skills in coffee preparation, aiding research in manipulation learning and policy development.

Contribution

The paper introduces COFFAIL, a diverse dataset of robot skill executions including anomalies, and demonstrates its use in imitation learning for robot policy development.

Findings

Dataset includes both successful and anomalous episodes.

Demonstrates use of dataset for imitation learning.

Includes bimanual manipulation examples.

Abstract

In the context of robot learning for manipulation, curated datasets are an important resource for advancing the state of the art; however, available datasets typically only include successful executions or are focused on one particular type of skill. In this short paper, we briefly describe a dataset of various skills performed in the context of coffee preparation. The dataset, which we call COFFAIL, includes both successful and anomalous skill execution episodes collected with a physical robot in a kitchen environment, a couple of which are performed with bimanual manipulation. In addition to describing the data collection setup and the collected data, the paper illustrates the use of the data in COFFAIL to learn a robot policy using imitation learning.