ExpressMM: Expressive Mobile Manipulation Behaviors in Human-Robot Interactions

Mobile manipulators are increasingly deployed in human-centered environments to perform tasks. While completing such tasks, they should also be able to communicate their intent to the people around them using expressive robot behaviors. Prior work on expressive robot behaviors has used preprogrammed or learning-from-demonstration- based expressive motions and large language model generated high-level interactions. The majority of these existing approaches have not considered human-robot interactions (HRI) where users may interrupt, modify, or redirect a robot's actions during task execution. In this paper, we develop the novel ExpressMM framework that integrates a high-level language-guided planner based on a vision-language model for perception and conversational reasoning with a low-level vision-language-action policy to generate expressive robot behaviors during collaborative HRI tasks. Furthermore, ExpressMM supports interruptible interactions to accommodate updated or redirecting instructions by users. We demonstrate ExpressMM on a mobile manipulator assisting a human in a collaborative assembly scenario and conduct audience-based evaluation of live HRI demonstrations. Questionnaire results show that the ExpressMM-enabled expressive behaviors helped observers clearly interpret the robot's actions and intentions while supporting socially appropriate and understandable interactions. Participants also reported that the robot was useful for collaborative tasks and behaved in a predictable and safe manner during the demonstrations, fostering positive perceptions of the robot's usefulness, safety, and predictability during the collaborative tasks.

PovNet+: A Deep Learning Architecture for Socially Assistive Robots to Learn and Assist with Multiple Activities of Daily Living

A significant barrier to the long-term deployment of autonomous socially assistive robots is their inability to both perceive and assist with multiple activities of daily living (ADLs). In this paper, we present the first multimodal deep learning architecture, POVNet+, for multi-activity recognition for socially assistive robots to proactively initiate assistive behaviors. Our novel architecture introduces the use of both ADL and motion embedding spaces to uniquely distinguish between a known ADL being performed, a new unseen ADL, or a known ADL being performed atypically in order to assist people in real scenarios. Furthermore, we apply a novel user state estimation method to the motion embedding space to recognize new ADLs while monitoring user performance. This ADL perception information is used to proactively initiate robot assistive interactions. Comparison experiments with state-of-the-art human activity recognition methods show our POVNet+ method has higher ADL classification accuracy. Human-robot interaction experiments in a cluttered living environment with multiple users and the socially assistive robot Leia using POVNet+ demonstrate the ability of our multi-modal ADL architecture in successfully identifying different seen and unseen ADLs, and ADLs being performed atypically, while initiating appropriate assistive human-robot interactions.

The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare

The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health specific LLM based robots in terms of multi modal communication through human robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.