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Affordance-based altruistic robotic architecture for human-robot collaboration

机译：基于带来的人类机器人协作的可供选择性机器人架构

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This article proposes a computational model for altruistic behavior, shows its implementation on a physical robot, and presents the results of human-robot interaction experiments conducted with the implemented system. Inspired from the sensorimotor mechanisms of the primate brain, object affordances are utilized for both intention estimation and action execution, in particular, to generate altruistic behavior. At the core of the model is the notion that sensorimotor systems developed for movement generation can be used to process the visual stimuli generated by actions of the others, infer the goals behind, and take the necessary actions to help achieving these goals, potentially leading to the emergence of altruistic behavior. Therefore, we argue that altruistic behavior is not necessarily a consequence of deliberate cognitive processing but may emerge through basic sensorimotor processes such as error minimization, that is, minimizing the difference between the observed and expected outcomes. In the model, affordances also play a key role by constraining the possible set of actions that an observed actor might be engaged in, enabling a fast and accurate intention inference. The model components are implemented on an upper-body humanoid robot. A set of experiments are conducted validating the workings of the components of the model, such as affordance extraction and task execution. Significantly, to assess how human partners interact with our altruistic model deployed robot, extensive experiments with naive subjects are conducted. Our results indicate that the proposed computational model can explain emergent altruistic behavior in reference to its biological counterpart and moreover engage human partners to exploit this behavior when implemented on an anthropomorphic robot.
机译：本文提出了针对利他行为的计算模型，显示其在物理机器人上的实现，并提出了用实施的系统进行的人机机器人交互实验的结果。灵感来自灵长类大脑的感觉电机机制，对目的的可征能力用于意图估计和动作执行，特别是产生利他行为。在模型的核心是概念，即用于运动代的传感器系统可以用于处理由其他动作产生的视觉刺激，推断后面的目标，并采取必要的行动来帮助实现这些目标，可能导致实现这些目标利他行为的出现。因此，我们认为利他行为不一定是故意认知处理的后果，而是可以通过诸如误差最小化的基本感觉运动过程中出现，即最小化观察和预期结果之间的差异。在模型中，通过约束观察到的演员可以参与的可能一组动作，可以发挥关键作用，从而实现快速和准确的意图推理。模型组件在上半身人形机器人上实现。进行一组实验，验证模型的组件的工作，例如承受的提取和任务执行。值得注意的是，为了评估人类合作伙伴如何与我们的利他模式部署机器人互动，进行了幼稚科目的广泛实验。我们的结果表明，所提出的计算模型可以参考其生物对应物来解释新的利他主义行为，而且接种人类合作伙伴在在人拟人机器人上实施时利用这种行为。

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