Preparing to move: Setting initial conditions to simplify interactions with complex objects.
Preparing to move: Setting initial conditions to simplify interactions with complex objects.
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DOI：
10.1371/journal.pcbi.1009597
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发表时间：
2021-12
影响因子：
4.3
通讯作者：
Sternad D
中科院分区：
文献类型：
--
作者：
Nayeem R;Bazzi S;Sadeghi M;Hogan N;Sternad D
人类灵巧地与各种物体互动，包括那些具有复杂内部动力学的物体。即使在端一杯咖啡的简单动作中，手不仅对杯子施加了力，而且间接地对液体施加了力，这消除了手的复杂反作用力。由于欠驱动和非线性，对象对动作的动态响应敏感地依赖于其初始状态，并且可以显示不可预测的，甚至是混沌行为。与总体假设，受试者争取可预测的对象的手的相互作用，本研究探讨了受试者如何探索和准备的动态对象，随后执行的目标任务。我们特别假设受试者在达到稳定和可预测的稳态之前找到缩短瞬变的初始条件。达到可预测的稳定状态是期望的，因为它可以减少对在线误差校正的需要并且便于前馈控制。备选假设是，受试者寻求减少努力，增加平滑度，并降低失败的风险。以“端咖啡”为任务，在虚拟环境中实现了一个简化的杯球模型。人类受试者通过提供力反馈的机器人操纵器与这个虚拟物体进行交互。受试者被鼓励首先探索和准备杯和球，然后在两个目标之间以指定的频率开始有节奏的运动，而不会丢失球。与假设相一致，受试者增加了手和物体之间的相互作用力的可预测性，并收敛到一组初始条件，然后显着减少瞬变。这三个备选假设没有得到支持。令人惊讶的是，受试者的策略更费力，更不流畅，不像观察到的简单伸手动作。杯球系统的逆动力学和阻抗控制器的正向仿真成功地描述了受试者的行为。受试者在实验中选择的初始条件与模拟中产生最可预测的相互作用的条件相匹配。这些结果首次支持了人类准备物体以最大限度地减少瞬变和增加稳定性的假设，以及总体上手-物体相互作用的可预测性。人类每天都与具有复杂动态的物体进行交互。举一个例子，拿起一个装满咖啡的杯子喝：手不仅对杯子施加了力，而且间接地对液体施加了力，而液体反过来又作用在手上。这些相互作用可能很快变得不可预测，即，使得人难以实时控制潜在的混乱交互。当从静止开始这样一个动作时，物体经历了部分取决于初始条件的启动动力学或瞬变。我们研究了受试者如何学习识别初始条件，以缩短瞬变，并使随后的相互作用动力学稳定和可预测的。使用一杯咖啡的简化模型，一个2D杯子，里面有一个滚动的球，并在水平线上移动，鼓励受试者准备物体动力学，以找到最佳的初始条件，然后开始一个连续的运输任务。实验和建模结果表明，与实践科目收敛到一个小的初始条件，这减少了他们的瞬态持续时间，并实现越来越可预测的相互作用。不受约束的任务中的典型目标--减少工作量和增加流畅性--无法得到支持。
Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object’s dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of ‘carrying a cup of coffee’, a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects’ strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects’ behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions. Humans interact with objects that have complex dynamics every day. One example is picking up a cup filled with coffee to drink: the hand applies a force not only to the cup, but also indirectly to the liquid, which in turn acts back on the hand. These interactions can quickly become unpredictable, i.e., making it difficult for a person to control the potentially chaotic interactions in real-time. When starting such an action from rest, the object experiences start-up dynamics, or transients, that partly depend upon the initial conditions. We examined how subjects learn to identify initial conditions to shorten transients and make the ensuing interaction dynamics stable and predictable. Using a simplified model of the cup of coffee, a 2D cup with a rolling ball inside and moving on a horizontal line, subjects were encouraged to prepare the object dynamics to find the best initial conditions prior to starting a continuous transport task. Experimental and modeling results showed that with practice subjects converged to a small set of initial conditions, which decreased their transient durations, and achieved increasingly predictable interactions. The typical objectives in unconstrained tasks—to decrease effort and increase smoothness—could not be supported.
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DOI：
10.1080/01691864.2020.1777198
发表时间：
2020
期刊：
Advanced robotics : the international journal of the Robotics Society of Japan
影响因子：
--
作者：
Bazzi S;Sternad D
通讯作者：
Sternad D
影响因子：
2.5
作者：
Danion, Frederic;Diamond, Jonathan S.;Flanagan, J. Randall
通讯作者：
Flanagan, J. Randall
影响因子：
2.1
作者：
Amazeen, EL;Sternad, D;Turvey, MT
通讯作者：
Turvey, MT
影响因子：
4.7
作者：
de Paula, Aline Souza;Savi, Marcelo Amorim;Pereira-Pinto, Francisco Heitor Lunes
通讯作者：
Pereira-Pinto, Francisco Heitor Lunes
影响因子：
2.5
作者：
Cluff, Tyler;Manos, Aspasia;Balasubramaniam, Ramesh
通讯作者：
Balasubramaniam, Ramesh