Anticipation and initiative in human-humanoid interaction
Anticipation and initiative in human-humanoid interaction
复制标题
人机交互中的预期和主动性
DOI：
10.1109/ichr.2008.4755974
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
L. Natale
中科院分区：
文献类型：
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作者：
Peter Ford Dominey;G. Metta;F. Nori;L. Natale
人形机器人的长期目标之一是让这些机器人与人类并肩工作，帮助人类完成各种可以实时变化的开放式任务。在这种情况下，机器人行为的一个关键组成部分将是尽快适应可以从人类身上学到的规律。这将使机器人能够预测可预测的事件，从而使交互更加流畅。这对于将重复多次的任务或包含将在全局任务中重复的子任务的上下文尤其相关。通过重复，机器人应该自动提取和利用潜在的规律。在这里，我们展示了在协作组装任务的背景下人机协作实验的结果。该架构的特点是维护和使用“交互历史记录”——过去发生的所有交互的文字记录。在在线交互过程中，系统不断搜索交互历史记录，以查找其起始时间与当前正在调用的操作相匹配的序列。对此类匹配的识别使机器人能够采取不同级别的预期活动。随着用户不断验证预测的序列，预期和学习的水平就会增加。 1 级预期允许系统预测用户会说什么，从而无需在预测成立时进行验证。 2 级允许系统主动提出预测的下一个事件。在第 3 级，机器人非常有信心并主动执行预测的动作。我们演示了这些渐进级别如何使合作交互更加流畅和快速。讨论了进一步完善人机合作质量的影响。
One of the long-term goals for humanoid robotics is to have these robots working side-by side with humans, helping the humans in a variety of open ended tasks, which can change in real-time. In such contexts a crucial component of the robot behavior will be to adapt as rapidly as possible to regularities that can be learned from the human. This will allow the robot to anticipate predictable events, in order to render the interaction more fluid. This will be particularly pertinent in the context of tasks that will be repeated several times, or that contain sub-tasks that will be repeated within the global task. Through exposure to repetition the robot should automatically extract and exploit the underlying regularities. Here we present results from human-robot cooperation experiments in the context of a cooperative assembly task. The architecture is characterized by the maintenance and use of an ldquointeraction historyrdquo - a literal record of all past interactions that have taken place. During on-line interaction, the system continuously searches the interaction history for sequences whose onset matches the actions that are currently being invoked. Recognition of such matches allows the robot to take different levels of anticipatory activity. As predicted sequences are successively validated by the user, the level of anticipation and learning increases. Level 1 anticipation allows the system to predict what the user will say, and thus eliminate the need for verification when the prediction holds. At Level 2 allows the system to take initiative to propose the predicted next event. At Level 3, the robot is highly confident and takes initiative to perform the predicted action. We demonstrate how these progressive levels render the cooperative interaction more fluid and more rapid. Implications for further refinement in the quality of human-robot cooperation are discussed.