Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed; the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot’s velocity based on the following inputs; the patient’s gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.

The paper proposes a robot learning framework that empowers a robot to automatically generate a sequence of actions from unstructured spoken language. The robot learning framework was able to distinguish between instructions and unrelated conversations. Data were collected from 25 participants, who were asked to instruct the robot to perform a collaborative cooking task while being interrupted and distracted. The system was able to identify the sequence of instructed actions for a cooking task with an accuracy of of 92.85 ± 3.87%.

This research adopts a multidisciplinary approach, synthesizing insights from psychology, technology, and ethics to unravel the intricate threads of diverse gender perceptions regarding trust-building, privacy considerations, and safety concerns in Human-Robot Interaction (HRI). Our study contributes to a holistic understanding of HRI dynamics, providing valuable insights for designing robots to assist individuals with their Activities of Daily Living (ADL) at home, including tasks such as preparing their daily meals independently. This study delves into the correlation between robot failures and gender perceptions of trust, privacy, and safety when a human communicates with a robot in a natural way by using unstructured speech. In this approach, the user commands the robot conversationally using natural spoken language to fetch cooking-related items in a research lab’s mocked-up kitchen. With a participant pool of 35 adults (13 females with an average age of 35.58 ± 12.06 and 22 males with an average age of 35.68 ± 15.35), Kendall’s Tau correlations are employed for statistical analysis, offering a comprehensive investigation into the intricate interplay of gender, interaction methods, and perceptions in the realm of human-robot dynamics.

This research delves into user preferences concerning structured (the subject follows an exact script to command the robot) and unstructured (the subject commands the robot in a conversational way) robot interaction through natural spoken language. Data was gathered from 30 adult participants who completed two distinct tasks involving both structured and unstructured commands. The study examines correlations between robot errors and user perceptions, as well as how past or present failures impact participants’ perception of robot utility. Three hypotheses are formulated, and the paper offers a comprehensive overview of the study’s aims, methodologies, and principal findings, which were ascertained using paired t-Test and Kendall-Tau correlations. The study indicates that participants showed a preference for the unstructured task in contrast to the structured one. Analysis of the data revealed interesting correlations between the user perception of the robot and the robot errors. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible