1 research outputs found
Human-to-Robot Attention Transfer for Robot Execution Failure Avoidance Using Stacked Neural Networks
Due to world dynamics and hardware uncertainty, robots inevitably fail in
task executions, leading to undesired or even dangerous executions. To avoid
failures for improved robot performance, it is critical to identify and correct
robot abnormal executions in an early stage. However, limited by reasoning
capability and knowledge level, it is challenging for a robot to self diagnose
and correct their abnormal behaviors. To solve this problem, a novel method is
proposed, human-to-robot attention transfer (H2R-AT) to seek help from a human.
H2R-AT is developed based on a novel stacked neural networks model,
transferring human attention embedded in verbal reminders to robot attention
embedded in robot visual perceiving. With the attention transfer from a human,
a robot understands what and where human concerns are to identify and correct
its abnormal executions. To validate the effectiveness of H2R-AT, two
representative task scenarios, "serve water for a human in a kitchen" and "pick
up a defective gear in a factory" with abnormal robot executions, were designed
in an open-access simulation platform V-REP; volunteers were recruited to
provide about 12000 verbal reminders to learn and test the attention transfer
model H2R-AT. With an accuracy of in transferring attention and
accuracy of in avoiding robot execution failures, the effectiveness
of H2R-AT was validated.Comment: 6 pages, 9 figure