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Human control of complex objects: towards more dexterous robots
Advanced Robotics ( IF 1.4 ) Pub Date : 2020-06-16
Salah Bazzi, Dagmar Sternad

Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at ‘tool use’ and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex – underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human–robot interaction.



中文翻译:

人工控制复杂物体:转向更灵巧的机器人

对于动力不足的机器人,操纵物体仍然是一个挑战。相反,人类擅长“工具使用”,并且对人类控制策略的更多了解可能会为机器人控制体系结构提供信息。我们检查了人类对表现出复杂的对象的控制-驱动不足,非线性以及潜在的混沌动力学,例如运输一杯咖啡。适用于无限制运动的简单控制策略(例如使平滑度最大化)失败了,因为必须补偿或抢占相互作用力。但是,当对象具有非线性和不可预测的动力学特性时,基于内部模型的预测控制将显得艰巨。我们假设人类学习使这些相互作用可预测的策略。在虚拟环境中,受试者使用机器人视觉和触觉界面与虚拟杯子和滚球互动。两种不同的度量标准量化了可预测性:稳定性或收缩性,以及控制器和对象之间的相互信息。在点对点位移中,受试者利用物体动力学的收缩区域来安全地导航扰动。控制收缩指标表明,受试者使用的控制器可以使轨迹呈指数稳定。在连续的杯和球移位过程中,受试者开发了可预测的解决方案,从而牺牲了平滑度和能效。这些结果可能会刺激灵巧机器人操纵器和人机交互的控制策略。以及控制器和对象之间的相互信息。在点对点位移中,受试者利用物体动力学的收缩区域来安全地导航扰动。控制收缩指标表明,受试者使用的控制器可以使轨迹呈指数稳定。在连续的杯和球移位过程中,受试者开发了可预测的解决方案,从而牺牲了平滑度和能效。这些结果可能会刺激灵巧机器人操纵器和人机交互的控制策略。以及控制器和对象之间的相互信息。在点对点位移中,受试者利用物体动力学的收缩区域来安全地导航扰动。控制收缩指标表明,受试者使用的控制器可以使轨迹呈指数稳定。在连续的杯和球移位过程中,受试者开发了可预测的解决方案,从而牺牲了平滑度和能效。这些结果可能会刺激灵巧机器人操纵器和人机交互的控制策略。在连续的杯和球移位过程中,受试者开发了可预测的解决方案,从而牺牲了平滑度和能效。这些结果可能会刺激灵巧机器人操纵器和人机交互的控制策略。在连续的杯和球移位过程中,受试者开发了可预测的解决方案,从而牺牲了平滑度和能效。这些结果可能会刺激灵巧机器人操纵器和人机交互的控制策略。

更新日期:2020-06-16
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