1,321 research outputs found
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming
The rapid advancements in artificial intelligence (AI) have led to a growing
trend of human-AI teaming (HAT) in various fields. As machines continue to
evolve from mere automation to a state of autonomy, they are increasingly
exhibiting unexpected behaviors and human-like cognitive/intelligent
capabilities, including situation awareness (SA). This shift has the potential
to enhance the performance of mixed human-AI teams over all-human teams,
underscoring the need for a better understanding of the dynamic SA interactions
between humans and machines. To this end, we provide a review of leading SA
theoretical models and a new framework for SA in the HAT context based on the
key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA)
framework unifies human and AI behavior, and involves bidirectional, and
dynamic interaction. The framework is based on the individual and team SA
models and elaborates on the cognitive mechanisms for modeling HAT. Similar
perceptual cycles are adopted for the individual (including both human and AI)
and the whole team, which is tailored to the unique requirements of the HAT
context. ATSA emphasizes cohesive and effective HAT through structures and
components, including teaming understanding, teaming control, and the world, as
well as adhesive transactive part. We further propose several future research
directions to expand on the distinctive contributions of ATSA and address the
specific and pressing next steps.Comment: 52 pages,5 figures, 1 tabl
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