Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation

Abstract

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.

Implementation of haptic communication in comanipulative tasks: A statistical state machine model

“…The objective of studying the way humans cooperate in co-manipulation situations is to be able to replicate these behaviors, or at least understand them well enough to help robots adapt to humans. Reed et al [4] demonstrated that in one degree-of-freedom pointing tasks, humans dyads performed better than individuals, result confirmed in several other studies [5] [6] [7] [8]. They also observed that replicating human behavior could be easily done and can reduce the perceived difference between a human partner and a robotic partner.…”

Analysis of Rigid Extended Object Co-Manipulation by Human Dyads: Lateral Movement Characterization

“…Some research involved shared virtual-environment loads [23], [24], and others involved upper arm movements of individuals and dyads [25], [18]. These experiments clarify many aspects of pHRI, including verifying that haptic information aids in co-manipulation tasks, noting some interaction patterns, and combining planning and learning to complete goal-oriented tasks.…”

Human Behavioral Metrics of a Predictive Model Emerging During Robot Assisted Following Without Visual Feedback

“…There are several metrics used to assess the effectiveness of human-robot interactions [10], [11]. In [10] authors present analysis of humanrobot co-operative load transport to adjust the robot's urge to complete the task based on the human feedback.…”

“…In this study, the authors introduce metrics that quantify mass and grasp. Moreover, the study to understand the interaction patterns involving two or more humans in the physical task, a confusion matrix was used to classify some behavioural patterns [11]. Studies done in morphological computation [12] show that perception and action are related through a shared embodiment [13], [14].…”