Designing a minimalist socially aware robotic agent for the home

TL;DR

This paper introduces a minimalist social robot that uses simple, low-resolution sensors and machine learning to recognize social scenarios in a home environment, aiming to create socially aware robotic agents.

Contribution

It presents a novel approach to social robotics using low-resolution sensor data and machine learning for activity recognition in a minimalist robot platform.

Findings

High accuracy detection of social scenarios using low-resolution sensors

Successful classification of three social scenarios from over a hundred experiments

Demonstration of the feasibility of minimalist sensors for social activity recognition

Abstract

We present a minimalist social robot that relies on long timeseries of low resolution data such as mechanical vibration, temperature, lighting, sounds and collisions. Our goal is to develop an experimental system for growing socially situated robotic agents whose behavioral repertoire is subsumed by the social order of the space. To get there we are designing robots that use their simple sensors and motion feedback routines to recognize different classes of human activity and then associate to each class a range of appropriate behaviors. We use the Katie Family of robots, built on the iRobot Create platform, an Arduino Uno, and a Raspberry Pi. We describe its sensor abilities and exploratory tests that allow us to develop hypotheses about what objects (sensor data) correspond to something known and observable by a human subject. We use machine learning methods to classify three social scenarios from over a hundred experiments, demonstrating that it is possible to detect social situations with high accuracy, using the low-resolution sensors from our minimalist robot.