Manizheh Zand

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.

For people who are blind or visually impaired, it is complicated to attend an in-person professional event, such as workshops or conferences, and initiate conversations with other attendees. This paper proposes TalkConnect, a novel mobile application that aims to empower individuals with visual impairments or blindness to identify other participants in the event, initiate communication, and request in-person meetings. The prototype of the app is presented, and its design and characteristics are discussed.

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. Abstract-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.

Robots have the potential to assist people in daily tasks, such as cooking a meal. Communicating with the robots verbally and in an unstructured way is important, as spoken language is the main form of communication for humans. This paper proposes a novel framework that automatically generates robot actions from unstructured speech. The proposed frame-work was evaluated by collecting data from 15 participants preparing their meals while seating on a chair in a randomly disrupted environment. The system can identify and respond to a task sequence while the user may be engaged in unrelated conversations, even if the user’s speech might be unstructured and grammatically incorrect. The accuracy of the proposed system is 98.6%, which is a very promising finding.

In recent years, advances in accessible technology have shown their potential to support and assist people with vision impairment. Current State of the Art Research is focused on Sensory Substitution Designs or Devices (SSDs) for assisting people with visual impairments. Moreover, research focuses on accommodating the individuals’ perceptions, sensations, and emotions by incorporating multisensory integration, relatable music, and Ultrasonic Haptic Feedback. This paper discusses the current State of the Art and identifies the gaps in assistive technologies for people with visual impairment. Moreover, this paper proposes a framework that will provide multi-sensory feedback to enhance the person’s experience in a museum and their perception of art paintings.

he 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 the accuracy of \AverageAccuracy%.