TAILOR: Teaching with Active and Incremental Learning for Object Registration

TL;DR

TAILOR is a system that enables robots to efficiently learn to recognize new objects through active viewpoint exploration and incremental learning, reducing training time and avoiding forgetting previous knowledge.

Contribution

It introduces a novel active and incremental learning approach for object registration, allowing robots to autonomously select informative views and learn incrementally without forgetting.

Findings

Successfully learned novel objects in real-world tasks

Reduced training time through active viewpoint selection

Avoided forgetting of previously learned objects

Abstract

When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and labor-intensive. We present TAILOR -- a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions.