Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics

TL;DR

This paper presents an adaptive control framework enabling a mobile robot to collaboratively transport objects with unknown deformation characteristics, effectively integrating haptic and kinematic data for intuitive human-robot interaction.

Contribution

The novel framework adaptively manages unknown object deformability using haptic and motion data, improving human-robot collaboration in object transportation tasks.

Findings

Framework effectively handles deformable and rigid objects.

Experimental results show improved collaboration and assistance.

Quantitative and qualitative assessments confirm robustness.

Abstract

In this work, we introduce an adaptive control framework for human-robot collaborative transportation of objects with unknown deformation behaviour. The proposed framework takes as input the haptic information transmitted through the object, and the kinematic information of the human body obtained from a motion capture system to create reactive whole-body motions on a mobile collaborative robot. In order to validate our framework experimentally, we compared its performance with an admittance controller during a co-transportation task of a partially deformable object. We additionally demonstrate the potential of the framework while co-transporting rigid (aluminum rod) and highly deformable (rope) objects. A mobile manipulator which consists of an Omni-directional mobile base, a collaborative robotic arm, and a robotic hand is used as the robotic partner in the experiments. Quantitative and qualitative results of a 12-subjects experiment show that the proposed framework can effectively deal with objects of unknown deformability and provides intuitive assistance to human partners.