Department of Mechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA; Laboratory for Computational Sensing and Robotics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Curr Biol. 2024 May 20;34(10):2118-2131.e5. doi: 10.1016/j.cub.2024.04.019. Epub 2024 Apr 30.
Humans and other animals can readily learn to compensate for changes in the dynamics of movement. Such changes can result from an injury or changes in the weight of carried objects. These changes in dynamics can lead not only to reduced performance but also to dramatic instabilities. We evaluated the impacts of compensatory changes in control policies in relation to stability and robustness in Eigenmannia virescens, a species of weakly electric fish. We discovered that these fish retune their sensorimotor control system in response to experimentally generated destabilizing dynamics. Specifically, we used an augmented reality system to manipulate sensory feedback during an image stabilization task in which a fish maintained its position within a refuge. The augmented reality system measured the fish's movements in real time. These movements were passed through a high-pass filter and multiplied by a gain factor before being fed back to the refuge motion. We adjusted the gain factor to gradually destabilize the fish's sensorimotor loop. The fish retuned their sensorimotor control system to compensate for the experimentally induced destabilizing dynamics. This retuning was partially maintained when the augmented reality feedback was abruptly removed. The compensatory changes in sensorimotor control improved tracking performance as well as control-theoretic measures of robustness, including reduced sensitivity to disturbances and improved phase margins.
人类和其他动物可以很容易地学会补偿运动动力学的变化。这种变化可能是由于受伤或携带物体重量的变化引起的。这些动力学变化不仅会导致性能下降，还会导致明显的不稳定性。我们评估了控制策略中的补偿变化对 Eigenmannia virescens（一种弱电鱼）的稳定性和鲁棒性的影响。我们发现，这些鱼会根据实验产生的不稳定性动态来调整其感觉运动控制系统。具体来说，我们使用增强现实系统在图像稳定任务中操纵感官反馈，在该任务中，鱼保持在避难所内的位置。增强现实系统实时测量鱼的运动。这些运动通过高通滤波器进行过滤，并乘以增益系数，然后反馈到避难所运动。我们调整增益系数以逐渐破坏鱼的感觉运动循环。鱼会调整其感觉运动控制系统以补偿实验引起的不稳定性动态。当突然去除增强现实反馈时，这种调整部分得到维持。感觉运动控制的补偿变化提高了跟踪性能以及控制理论鲁棒性的度量，包括对干扰的敏感性降低和相位裕度提高。
