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NRI: Collaborative Research: Towards Robots with Human Dexterity

NRI：协作研究：迈向具有人类灵活性的机器人

基本信息

批准号：
1637854
负责人：
Dagmar Sternad
金额：
$ 50万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637854&HistoricalAwards=false
关键词：
NRI Collaborative Research Towards Robots

项目摘要

Despite vastly slower "hardware" and "wetware," human dexterity vastly out-performs modern robots. This project studies apparently-simple tasks - managing the kinematic constraint on hand motion required to open a door; and dealing with the dynamic complexity of liquid sloshing in a cup of coffee - that profoundly challenge robots but humans perform with ease. The key idea is that humans manage skillful physical interaction with these objects by exploiting clever combinations of primitive dynamic actions that do not require continuous intervention. A novel theory to describe the effectiveness of this approach is developed and tested by experiments with human subjects. The theory is applied to transfer comparable skill to robots, despite manifestly different hardware. If successful, these robots will be more capable, more comprehensible, and more collaborative partners with humans.The central experimental challenge is to determine the essential strategy underlying humans? remarkable competence in physical interaction tasks. Three hypotheses reflecting major themes in contemporary motor neuroscience are tested: Humans 1) develop models of object dynamics sufficient to pre-compute and execute required hand motions (similar to modern robot programming); 2) choose forces and motions to minimize muscular effort (similar to optimizing efficiency); or 3) exploit dynamic primitives to robustly achieve satisficing (good-enough) performance. The theoretical challenge is to formulate a coherent account combining the information-processing of brains (or computers) with the "energy-processing" of physical objects and their interactions. Classical equivalent circuit theory is re-purposed to define a neo-classical equivalent network theory, combining dynamic motion primitives with mechanical impedances (interactive dynamics). Mechanical impedances enjoy a key property, compositionality, that overcomes the curse of dimensionality.

尽管“硬件”和“湿件”速度慢得多，但人类的灵巧性大大超过了现代机器人。该项目研究看似简单的任务-管理开门所需的手部运动的运动学约束;以及处理一杯咖啡中液体晃动的动态复杂性-这对机器人提出了深刻的挑战，但人类可以轻松完成。关键的想法是，人类通过利用不需要持续干预的原始动态动作的巧妙组合来管理与这些对象的熟练物理交互。一个新的理论来描述这种方法的有效性，并与人类受试者的实验进行了测试。该理论被应用于将类似的技能转移给机器人，尽管硬件明显不同。如果成功，这些机器人将更有能力，更容易理解，更与人类合作的伙伴。在物理互动任务中表现出色。测试了反映当代运动神经科学主要主题的三个假设：人类1）开发足以预先计算和执行所需手部运动的对象动力学模型（类似于现代机器人编程）; 2）选择力和运动以最大限度地减少肌肉努力（类似于优化效率）;或3）利用动态原语来鲁棒地实现令人满意的（足够好的）性能。理论上的挑战是，要将大脑（或计算机）的信息处理与物理对象的“能量处理”及其相互作用结合起来，形成一个连贯的解释。经典等效电路理论被重新定义为新古典等效网络理论，将动态运动基元与机械阻抗（交互动力学）相结合。机械阻抗有一个关键的特性，即组合性，它克服了维数灾难。