From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness

TL;DR

This paper explores how a humanoid robot can infer whether a human partner is careful during object manipulation using only visual data, achieving high accuracy and enabling more adaptive and socially aware robotic behaviors.

Contribution

It introduces a method for online recognition of human carefulness from vision, advancing robotic perception and social interaction capabilities.

Findings

High accuracy (up to 81.3%) in carefulness recognition from low-resolution video.

Recognition effectiveness decreases for short movements without obstacles.

Enables robots to adapt actions based on perceived human carefulness.

Abstract

When manipulating objects, humans finely adapt their motions to the characteristics of what they are handling. Thus, an attentive observer can foresee hidden properties of the manipulated object, such as its weight, temperature, and even whether it requires special care in manipulation. This study is a step towards endowing a humanoid robot with this last capability. Specifically, we study how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. Only for short movements without obstacles, carefulness recognition was insufficient. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.