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Mightability Maps: A Perceptual Level Decisional Framework for Co-operative and Competitive Human-Robot Interaction

机译:可变地图:具有竞争性和竞争性人员机器人互动的感知水平策略框架

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Interestingly Humans are able to maintain rough estimations of visibility, reachability and other capabilities of not only themselves but of the person they are interacting with. Studies in neuroscience and psychology suggest that from the age of 12-15 months children start to understand the occlusion of others line-of-sight and from the age of 3 years they start to develop the ability, termed as perceived reachability for self and for others. As such capabilities evolve in the children, they start showing intuitive and proactive behavior by perceiving various abilities of the human partner. Inspired from such studies, which suggest that visuo-spatial perception plays an important role in Human-Human interaction, we propose to equip our robot with the capabilities to maintain various types of reachabilities and visibilities information of itself and of the human partner in the shared workspace. Since these analyses will be basically perceived by performing a virtual action onto the agent and roughly estimating what that agent might be able to 'see' and 'reach' in 3D space, we term these representations as Mightability Maps. By applying various set operations on Weighted Mightability Maps, robot could perceive a set of candidate solutions in real time for various tasks. We show its application in exhibiting two different behaviors of robot: co-operative and competitive. These maps are also quick to compute and could help in developing higher-level decisional capabilities in the robot.
机译:有趣的人类能够保持对不仅自身的知识,可达性和其他能力的粗略估计,而是他们正在互动的人。神经科学和心理学的研究表明,从12-15个月的儿童开始了解他人的闭塞,从3年来开始,他们开始发展这种能力,被称为自我和自我的可达性其他。由于这些能力在儿童中进化,他们开始通过察觉人类伴侣的各种能力来表现出直观和主动的行为。从这些研究中启发了这一研究,该研究表明,潜视空间感知在人类互动中发挥着重要作用,我们建议将我们的机器人配备了维持各种类型的可遗产和社会伴侣的信息和人类伴侣的能力工作区。由于这些分析将基本上通过对代理执行虚拟动作并大致估计该代理商可以在3D空间中“看到”和“达到”的情况来感知,因此将这些表示作为强制性映射术语。通过对加权可变性地图应用各种集合操作,机器人可以在各种任务中实时识别一组候选解决方案。我们展示了展示两种不同行为的机器人:合作和竞争力。这些地图也很快计算,并有助于在机器人中开发更高级别的抗议功能。

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