401 research outputs found
Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning
Multi-human multi-robot teams have great potential for complex and
large-scale tasks through the collaboration of humans and robots with diverse
capabilities and expertise. To efficiently operate such highly heterogeneous
teams and maximize team performance timely, sophisticated initial task
allocation strategies that consider individual differences across team members
and tasks are required. While existing works have shown promising results in
reallocating tasks based on agent state and performance, the neglect of the
inherent heterogeneity of the team hinders their effectiveness in realistic
scenarios. In this paper, we present a novel formulation of the initial task
allocation problem in multi-human multi-robot teams as contextual
multi-attribute decision-make process and propose an attention-based deep
reinforcement learning approach. We introduce a cross-attribute attention
module to encode the latent and complex dependencies of multiple attributes in
the state representation. We conduct a case study in a massive threat
surveillance scenario and demonstrate the strengths of our model.Comment: Accepted to IROS202
Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation
Socially aware robot navigation, where a robot is required to optimize its
trajectory to maintain comfortable and compliant spatial interactions with
humans in addition to reaching its goal without collisions, is a fundamental
yet challenging task in the context of human-robot interaction. While existing
learning-based methods have achieved better performance than the preceding
model-based ones, they still have drawbacks: reinforcement learning depends on
the handcrafted reward that is unlikely to effectively quantify broad social
compliance, and can lead to reward exploitation problems; meanwhile, inverse
reinforcement learning suffers from the need for expensive human
demonstrations. In this paper, we propose a feedback-efficient active
preference learning approach, FAPL, that distills human comfort and expectation
into a reward model to guide the robot agent to explore latent aspects of
social compliance. We further introduce hybrid experience learning to improve
the efficiency of human feedback and samples, and evaluate benefits of robot
behaviors learned from FAPL through extensive simulation experiments and a user
study (N=10) employing a physical robot to navigate with human subjects in
real-world scenarios. Source code and experiment videos for this work are
available at:https://sites.google.com/view/san-fapl.Comment: To appear in IROS 202
Implications of Personality on Cognitive Workload, Affect, and Task Performance in Remote Robot Control
This paper explores how the personality traits of robot operators can
influence their task performance during remote control of robots. It is
essential to explore the impact of personal dispositions on information
processing, both directly and indirectly, when working with robots on specific
tasks. To investigate this relationship, we utilize the open-access multi-modal
dataset MOCAS to examine the robot operator's personality traits, affect,
cognitive load, and task performance. Our objective is to confirm if
personality traits have a total effect, including both direct and indirect
effects, that could significantly impact the performance levels of operators.
Specifically, we examine the relationship between personality traits such as
extroversion, conscientiousness, and agreeableness, and task performance. We
conduct a correlation analysis between cognitive load, self-ratings of workload
and affect, and quantified individual personality traits along with their
experimental scores. The findings show that personality traits do not have a
total effect on task performance.Comment: 8 pages, 6 figures, accepted to IROS 2023. A link to a supplementary
video is in the abstrac
Robotics and IoT: Interdisciplinary Applied Research in the RIoT Zone
Short Abstract:
Robotics and the Internet of Things are intrinsically multi-disciplinary subjects that investigate the interaction between the physical and the cyber worlds and how they impact society. As a result, they not only demand careful consideration of digital and analog technologies, but also the human element. The “RIoT Zone” brings together disparate people and ideas to address intuitive autonomy.
Full Abstract:
Robotics and the Internet of Things are intrinsically multi-disciplinary subjects that investigate the interaction between the physical and the cyber worlds and how they impact society. As a result, they not only demand careful consideration of digital and analog technologies, but also the human element. The “RIoT Zone” brings together disparate people and ideas to address a human-centric form of intelligence we call “intuitive autonomy”. This talk will describe human/robot interaction and the programming of robots by human demonstration from the perspectives of Engineering Technology, Computer Information Technology, Industrial Engineering and Psychology
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