Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

TL;DR

This paper investigates how humans communicate intent through force signals during collaborative manipulation and develops a real-time classifier to enable robots to interpret and respond to these signals, enhancing human-robot interaction.

Contribution

It introduces a novel force-kinematic feature set and a taxonomy for force communication, enabling robots to recognize human intent and take initiative in physical collaboration.

Findings

Force-kinematic features predict human intent effectively.

A real-time intent classifier was successfully developed.

The approach improves fluidity in human-robot physical interaction.

Abstract

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation.