Human Control of Interactions with Objects - Variability, Stability and Predictability
Human Control of Interactions with Objects - Variability, Stability and Predictability
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人类对与物体交互的控制——可变性、稳定性和可预测性
DOI：
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
D. Sternad
中科院分区：
文献类型：
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作者：
D. Sternad
人类如何控制自己的行为和与物理世界的互动？我们怎样才能学会扔一个球或喝一杯葡萄酒而不洒出来呢？与机器人相比，人类的灵活性仍然令人惊讶，特别是当缓慢的神经传输和高水平的噪音似乎困扰着生物系统时。人类的控制策略是什么，巧妙地导航，克服，甚至利用这些缺点？为了获得洞察力，我们提出了一种方法，集中在任务动态约束和启用（间）的行动。不可知论的控制器的细节，我们从一个物理模型的任务，允许充分理解的解决方案空间。在虚拟环境中渲染任务，我们研究人类如何学习满足复杂任务需求的解决方案。众多技能的核心是冗余，它允许探索和利用解决方案的子集。我们假设人类寻求对扰动稳定的解决方案，以使其内在的噪声问题更少。由于需要更少的校正，系统不太受反馈回路中长时间延迟的影响。三个实验范例，我们的方法：扔一个球的目标，有节奏的反弹球，并携带一个复杂的对象。对于投掷任务，结果表明，演员是敏感的错误容忍的任务。在有节奏的球弹跳中，受试者利用了桨球系统的动态稳定性。当操纵一杯葡萄酒时，受试者学习的策略使手与物体的互动更具可预测性。这些研究结果为控制器的发展提供了基础：我们认为，复杂的动作是由动态原语产生的，动态原语是具有吸引子稳定性的模块，对神经机械系统中的延迟和噪声不太敏感。
How do humans control their actions and interactions with the physical world? How do we learn to throw a ball or drink a glass of wine without spilling? Compared to robots human dexterity remains astonishing, especially as slow neural transmission and high levels of noise seem to plague the biological system. What are human control strategies that skillfully navigate, overcome, and even exploit these disadvantages? To gain insight we propose an approach that centers on how task dynamics constrain and enable (inter-)actions. Agnostic about details of the controller, we start with a physical model of the task that permits full understanding of the solution space. Rendering the task in a virtual environment, we examine how humans learn solutions that meet complex task demands. Central to numerous skills is redundancy that allows exploration and exploitation of subsets of solutions. We hypothesize that humans seek solutions that are stable to perturbations to make their intrinsic noise matter less. With fewer corrections necessary, the system is less afflicted by long delays in the feedback loop. Three experimental paradigms exemplify our approach: throwing a ball to a target, rhythmic bouncing of a ball, and carrying a complex object. For the throwing task, results show that actors are sensitive to the error-tolerance afforded by the task. In rhythmic ball bouncing, subjects exploit the dynamic stability of the paddle-ball system. When manipulating a “glass of wine”, subjects learn strategies that make the hand-object interactions more predictable. These findings set the stage for developing propositions about the controller: We posit that complex actions are generated with dynamic primitives, modules with attractor stability that are less sensitive to delays and noise in the neuro-mechanical system.
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影响因子：
--
作者：
Sternad D;Hasson CJ
通讯作者：
Hasson CJ
影响因子：
2.5
作者：
Kunlin Wei;T. Dijkstra;D. Sternad
通讯作者：
Kunlin Wei;T. Dijkstra;D. Sternad
混合节奏离散任务的最优控制：重温弹跳球
影响因子：
2.5
作者：
Ronsse, Renaud;Wei, Kunlin;Sternad, Dagmar
通讯作者：
Sternad, Dagmar
影响因子：
2.5
作者：
Hasson, Christopher J.;Shen, Tian;Sternad, Dagmar
通讯作者：
Sternad, Dagmar