Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.

Human-robot technology has the potential to positively impact the lives of people with motor disabilities. However, current efforts have mostly been oriented towards technology (sensors, devices, modalities, interaction techniques), thus relegating the user and their valuable input to the wayside. In this paper, we aim to present a holistic perspective of the role of participatory design in Human-Robot Collaboration (HRC) for People with Motor Disabilities (PWMD). We have been involved in several multiyear projects related to HRC for PWMD, where we encountered different challenges related to planning and participation, preferences of stakeholders, using certain participatory design techniques, technology exposure, as well as ethical, legal, and social implications. These challenges helped us provide five lessons learned that could serve as a guideline to researchers when using participatory design with vulnerable groups. In particular, early-career researchers who are starting to explore HRC research for people with disabilities.

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

Assistive technologies such as human-robot collaboration, have the potential to ease the life of people with physical mobility impairments in social and economic activities. Currently, this group of people has lower rates of economic participation, due to the lack of adequate environments adapted to their capabilities. We take a closer look at the needs and preferences of people with physical mobility impairments in a human-robot cooperative environment at the workplace. Specifically, we aim to design how to control a robotic arm in manufacturing tasks for people with physical mobility impairments. We present a case study of a sheltered-workshop as a prototype for an institution that employs people with disabilities in manufacturing jobs. Here, we collected data of potential end-users with physical mobility impairments, social workers, and supervisors using a participatory design technique (Future-Workshop). These stakeholders were divided into two groups, primary (end-users) and secondary users (social workers, supervisors), which were run across two separate sessions. The gathered information was analyzed using thematic analysis to reveal underlying themes across stakeholders. We identified concepts that highlight underlying concerns related to the robot fitting in the social and organizational structure, human-robot synergy, and human-robot problem management. In this paper, we present our findings and discuss the implications of each theme when shaping an inclusive human-robot cooperative workstation for people with physical mobility impairments.