Tell me what this is: Few-Shot Incremental Object Learning by a Robot

Abstract

For many applications, robots will need to be incrementally trained to recognize the specific objects needed for an application. This paper presents a practical system for incrementally training a robot to recognize different object categories using only a small set of visual examples provided by a human. The paper uses a recently developed state-of-the-art method for few-shot incremental learning of objects. After learning the object classes incrementally, the robot performs a table cleaning task organizing objects into categories specified by the human. We also demonstrate the system's ability to learn arrangements of objects and predict missing or incorrectly placed objects. Experimental evaluations demonstrate that our approach achieves nearly the same performance as a system trained with all examples at one time (batch training), which constitutes a theoretical upper bound.

arXiv commentAccepted at IEEE IROS 2020

References in corpus (1)

• Cognitively-Inspired Model for Incremental Learning Using a Few Examples

Cited by in corpus (6)

• Multimodal Parameter-Efficient Few-Shot Class Incremental Learning
• What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper
• Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions
• F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning
• CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics
• Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction