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Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain,...
Gone are the days of robots solely operating in isolation, without direct interaction with people. Rather, robots are increasingly being deployed in environments and roles that require complex social interaction with humans. The implementation of human-robot teams continues to increase as...
This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust....
Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step toward building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable...
Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority...
Convergent lines of evidence indicate that anthropomorphic robots are represented using neurocognitive mechanisms typically employed in social reasoning about other people. Relatedly, a growing literature documents that contexts of threat can exacerbate coalitional biases in social perceptions....
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