Tailoring Visual Object Representations to Human Requirements: A Case Study with a Recycling Robot

Debasmita Ghose, Michal Adam Lewkowicz, Kaleb Gezahegn, Julian Lee, Timothy Adamson, Marynel Vazquez, Brian Scassellati

Keywords: Representation Learning, Contrastive Learning, Human-in-the-loop Robot Learning

Abstract: Robots are well-suited to alleviate the burden of repetitive and tedious manipulation tasks. In many applications though, a robot may be asked to interact with a wide variety of objects, making it hard or even impossible to pre-program visual object classifiers suitable for the task of interest. In this work, we study the problem of learning a classifier for visual objects based on a few examples provided by humans. We frame this problem from the perspective of learning a suitable visual object representation that allows us to distinguish the desired object category from others. Our proposed approach integrates human supervision into the representation learning process by combining contrastive learning with an additional loss function that brings the representations of human examples close to each other in the latent space. Our experiments show that our proposed method performs better than self-supervised and fully supervised learning methods in offline evaluations and can also be used in real-time by a robot in a simplified recycling domain, where recycling streams contain a variety of objects.

Student First Author: yes

Website: https://sites.google.com/view/corl22-contrastive-recycling/home

Code: https://github.com/ScazLab/HumanSupContrastiveClustering