Communication is critical to collaboration;however, too much of it can degrade performance. Motivated by the need for effective use of a robot's communication modalities, in this work, we present a computational f...
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ISBN:
(纸本)9781450367462
Communication is critical to collaboration;however, too much of it can degrade performance. Motivated by the need for effective use of a robot's communication modalities, in this work, we present a computational framework that decides if, when, and what to communicate during human-robot collaboration. The framework, titled CommPlan, consists of a model specification process and an execution-time POMDP planner. To address the challenge of collecting interaction data, the model specification process is hybrid: where part of the model is learned from data, while the remainder is manually specified. Given the model, the robot's decision-making is performed computationally during interaction and under partial observability of human's mental states. We implement CommPlan for a shared workspace task, in which the robot has multiple communication options and needs to reason within a short time. Through experiments with human participants, we confirm that CommPlan results in the effective use of communication capabilities and improves human-robot collaboration.
We consider human-robot collaboration in sequential tasks with known task objectives. For interaction planning in this setting, the utility of models for decision-making under uncertainty has been demonstrated across ...
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We consider human-robot collaboration in sequential tasks with known task objectives. For interaction planning in this setting, the utility of models for decision-making under uncertainty has been demonstrated across domains. However, in practice, specifying the model parameters remains challenging, requiring significant effort from the robot developer. To alleviate this challenge, we present ADACORL, a framework to specify decision-making models and generate robot behavior for interaction. Central to our approach are a factored task model and a semi-supervised algorithm to learn models of human behavior. We demonstrate that our specification approach, despite significantly fewer labels, generates models (and policies) that perform equally well or better than models learned with supervised data. By leveraging pre-computed performance bounds and an online planner, ADACORL can generate robot behavior for collaborative tasks with large state spaces (> 1 million states) and short planning times (< 0.5 s).
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