Cheek to Chip: Dancing Robots and AI’s Future
Cheek to Chip: Dancing Robots and AI’s Future
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发表时间：
2008
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通讯作者：
J. Aucouturier;K. Ikeuchi;H. Hirukawa;Shin'ichiro Nakaoka;Takaaki Shiratori;S. Kudoh;F. Kanehiro;T. Ogata;H. Kozima;HIroshi G. Okuno;Marek P. Michalowski;Yuta Ogai;T. Ikegami;K. Kosuge;T. Takeda;Y. Hirata
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作者：
J. Aucouturier;K. Ikeuchi;H. Hirukawa;Shin'ichiro Nakaoka;Takaaki Shiratori;S. Kudoh;F. Kanehiro;T. Ogata;H. Kozima;HIroshi G. Okuno;Marek P. Michalowski;Yuta Ogai;T. Ikegami;K. Kosuge;T. Takeda;Y. Hirata
新一代的人形机器人在形状和关节能力上越来越类似于人类。这一进展促使研究人员设计能够模仿人类舞蹈的复杂性和风格的舞蹈机器人。此类复杂的操作通常是手动且临时编程的。然而，这种方法既乏味又不灵活。东京大学的研究人员开发了观察学习 (LFO) 训练方法来克服这一困难。1,2 LFO 使机器人能够通过观察人类演示来获取要做什么以及如何做的知识。由于被观察者和机器人之间存在动态和运动学差异（例如重量、平衡以及手臂和腿的长度），因此从人体关节角度到机器人关节角度的直接映射效果不佳。因此，LFO 依赖于预先设计的任务模型，该模型仅代表模仿所必需的动作（及其特征）。它使用任务模型来识别和解析人类行为的序列，例如，“现在，拿起盒子。”然后它会根据机器人的形态和动力学调整这些动作，以便它可以模仿运动。这种间接的两步映射对于稳健的模仿和性能至关重要。 LFO 已成功应用于各种手眼操作。1,2 这里我们描述如何将其扩展到跳舞的人形机器人。
Recent generations of humanoid robots increasingly resemble humans in shape and articulatory capacities. This progress has motivated researchers to design dancing robots that can mimic the complexity and style of human choreographic dancing. Such complicated actions are usually programmed manually and ad hoc. However, this approach is both tedious and inflexible. Researchers at the University of Tokyo have developed the learning-from-observation (LFO) training method to overcome this difficulty.1,2 LFO enables a robot to acquire knowledge of what to do and how to do it from observing human demonstrations. Direct mapping from human joint angles to robot joint angles doesn’t work well because of the dynamic and kinematic differences between the observed person and the robot (for example, weight, balance, and arm and leg lengths). LFO therefore relies on predesigned task models, which represent only the actions (and features thereof) that are essential to mimicry. It uses task models to recognize and parse the sequence of human actions—for example, “Now, pick up the box.” Then it adapts these actions to the robot’s morphology and dynamics so that it can mimic the movement. This indirect, two-step mapping is crucial for robust imitation and performance. LFO has been successfully applied to various hand-eye operations.1,2 Here we describe how to extend it to a dancing humanoid.