Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction.
CoRR(2023)
摘要
We focus on the problem of how we can enable a robot to collaborate
seamlessly with a human partner, specifically in scenarios like collaborative
manufacturing where prexisting data is sparse. Much prior work in human-robot
collaboration uses observational models of humans (i.e. models that treat the
robot purely as an observer) to choose the robot's behavior, but such models do
not account for the influence the robot has on the human's actions, which may
lead to inefficient interactions. We instead formulate the problem of optimally
choosing a collaborative robot's behavior based on a conditional model of the
human that depends on the robot's future behavior. First, we propose a novel
model-based formulation of conditional behavior prediction that allows the
robot to infer the human's intentions based on its future plan in data-sparse
environments. We then show how to utilize a conditional model for proactive
goal selection and path generation around human collaborators. Finally, we use
our proposed proactive controller in a collaborative task with real users to
show that it can improve users' interactions with a robot collaborator
quantitatively and qualitatively.
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