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Affordance-based altruistic robotic architecture for human-robot collaboration

机译：用于人机协作的基于负担的利他机器人架构

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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：本文提出了一种利他行为的计算模型，展示了它在物理机器人上的实现，并给出了在实现的系统上进行的人机交互实验的结果。受灵长类动物大脑感觉运动机制的启发，对象馈赠被用于意向估计和动作执行，特别是产生利他行为。该模型的核心思想是，为运动产生而开发的感觉运动系统可用于处理其他人的动作所产生的视觉刺激，推断背后的目标并采取必要的行动来帮助实现这些目标，从而有可能导致利他行为的出现。因此，我们认为利他行为不一定是故意认知过程的结果，而是可能通过基本的感觉运动过程而出现，例如误差最小化，即使观察到的结果与预期结果之间的差异最小。在该模型中，可负担能力还通过限制被观察者可能会采取的一系列行动来发挥关键作用，从而实现快速，准确的意图推断。模型组件在上身类人机器人上实现。进行了一组实验，以验证模型各组成部分的工作方式，例如能力抽取和任务执行。重要的是，为了评估人类伙伴如何与我们部署的利他模型机器人互动，我们对天真的对象进行了广泛的实验。我们的结果表明，提出的计算模型可以参考其生物学对应物来解释紧急的利他行为，而且可以在拟人化的机器人上实施时，促使人类伙伴利用这种行为。

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