62 research outputs found
The Effects of Voice Pitch on Perceptions of Robots
While previous research in human-robot interaction (HRI) has shown people respond similarly to vocal cues in robot speech as they do with human speech, there has been minimal research into the effects of voice pitch. This study investigates whether vocal pitch in robot speech will evoke stereotypical evaluations of the robot speaker. To explore this, multiple voices were synthesized from various text-to-speech applications and then manipulated to have a raised or lowered pitch. Participants will be asked to rate these voices on various scales such as competence, trustworthiness, and likeability. We expect our results to conform with the current literature on human voice pitch and encourage further research on the effects of the voice on HRI
Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments
In an efficient and flexible human-robot collaborative work environment, a
robot team member must be able to recognize both explicit requests and implied
actions from human users. Identifying "what to do" in such cases requires an
agent to have the ability to construct associations between objects, their
actions, and the effect of actions on the environment. In this regard, semantic
memory is being introduced to understand the explicit cues and their
relationships with available objects and required skills to make "tea" and
"sandwich". We have extended our previous hierarchical robot control
architecture to add the capability to execute the most appropriate task based
on both feedback from the user and the environmental context. To validate this
system, two types of skills were implemented in the hierarchical task tree: 1)
Tea making skills and 2) Sandwich making skills. During the conversation
between the robot and the human, the robot was able to determine the hidden
context using ontology and began to act accordingly. For instance, if the
person says "I am thirsty" or "It is cold outside" the robot will start to
perform the tea-making skill. In contrast, if the person says, "I am hungry" or
"I need something to eat", the robot will make the sandwich. A humanoid robot
Baxter was used for this experiment. We tested three scenarios with objects at
different positions on the table for each skill. We observed that in all cases,
the robot used only objects that were relevant to the skill.Comment: To Appear In International Conference on Intelligent Robots and
Systems (IROS), Detroit, MI, USA, Oct 202
A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
Cloud computing is a concept introduced in the information technology era,
with the main components being the grid, distributed, and valuable computing.
The cloud is being developed continuously and, naturally, comes up with many
challenges, one of which is scheduling. A schedule or timeline is a mechanism
used to optimize the time for performing a duty or set of duties. A scheduling
process is accountable for choosing the best resources for performing a duty.
The main goal of a scheduling algorithm is to improve the efficiency and
quality of the service while at the same time ensuring the acceptability and
effectiveness of the targets. The task scheduling problem is one of the most
important NP-hard issues in the cloud domain and, so far, many techniques have
been proposed as solutions, including using genetic algorithms (GAs), particle
swarm optimization, (PSO), and ant colony optimization (ACO). To address this
problem, in this paper, one of the collective intelligence algorithms, called
the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The
performance of the proposed algorithm has been compared with that of GAs, PSO,
continuous ACO, and the basic SSA. The results show that our algorithm has
generally higher performance than the other algorithms. For example, compared
to the basic SSA, the proposed method has an average reduction of approximately
21% in makespan.Comment: 15 pages, 6 figures, 2023 IFIP International Internet of Things
Conference. Dallas-Fort Worth Metroplex, Texas, US
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