A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

Abstract

Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.

Personalization of industrial human–robot communication through domain adaptation based on user feedback

“…In order to develop a personalized ML system, one must take advantage of the existence of publicly available large datasets that can provide feedback during continuous operation so that new data from the user can be labelled using that feedback. This mimics a key component of natural human communication wherein feedback is used to continually update the knowledge and status associated with the shared task (Mukherjee et al 2022c). The present document proposes a framework for such a continuous re-training leading to the development of personalized ML models for HRCom.…”

Personalization of Industrial Human-Robot Communication through Domain Adaptation based on User Feedback

“…This poses the constraint of developing a framework that can function on small datasets of VEnv data that must be curated to work with the well-performing ML model that has been trained on the communication mode that is to be used. This would allow us to leverage the capability of classification of complex gestures and further to personalize the ML models generated to the interacting human as presented in Mukherjee et al [2022b], Mukherjee [2023].…”

The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language

“…The exploration of human-robot interaction (HRI) [29], [32] and its advancement into real-world applications has been a topic of significant research over the past decades [30]. Current approaches for controlling and interacting with autonomous robots in the real 1 https://github.com/LinusNEP/TCC_IRoNL.git world have been dominated by complex teleoperation controllers [13], teach pendants [2], and rigid command protocols [16], where the robots execute predefined tasks based on specialized programming languages. As the challenges we present to these robots become more intricate and the environments they operate in grow more unpredictable [18], there arises an unmistakable need for more natural and intuitive interaction mechanisms.…”