# Understanding human co-manipulation via motion and haptic information to enable future physical human-robotic collaborations

## TL;DR

This paper studies how humans collaborate to move objects, focusing on motion and haptic cues to improve future human-robot teamwork.

## Contribution

The paper introduces a framework analyzing four sub-components of co-manipulation using motion and haptic data.

## Key findings

- Transitions between static and active states can be detected using motion and haptic signals.
- Certain signals like force and acceleration correlate strongly with team motion objectives.
- Task completion percentages reveal how haptic feedback can communicate motion goals.

## Abstract

Human teams intuitively and effectively collaborate to move large, heavy, or unwieldy objects. However, understanding of this interaction in literature is limited. This is especially problematic given our goal to enable human-robot teams to work together. Therefore, to better understand how human teams work together to eventually enable intuitive human-robot interaction, in this paper we examine four sub-components of collaborative manipulation (co-manipulation), using motion and haptics. We define co-manipulation as a group of two or more agents collaboratively moving an object. We present a study that uses a large object for co-manipulation as we vary the number of participants (two or three) and the roles of the participants (leaders or followers), and the degrees of freedom necessary to complete the defined motion for the object. In analyzing the results, we focus on four key components related to motion and haptics. Specifically, we first define and examine a static or rest state to demonstrate a method of detecting transitions between the static state and an active state, where one or more agents are moving toward an intended goal. Secondly, we analyze a variety of signals (e.g. force, acceleration, etc.) during movements in each of the six rigid-body degrees of freedom of the co-manipulated object. This data allows us to identify the best signals that correlate with the desired motion of the team. Third, we examine the completion percentage of each task. The completion percentage for each task can be used to determine which motion objectives can be communicated via haptic feedback. Finally, we define a metric to determine if participants divide two degree-of-freedom tasks into separate degrees of freedom or if they take the most direct path. These four components contribute to the necessary groundwork for advancing intuitive human-robot interaction.