Towards biomimetic control of robotic or paralyzed limbs

我们的长期目标是从生物运动中学习，为人工系统(机器人、假肢)和辅助人类控制(FES，神经接口)设计有效和新颖的控制策略。到目前为止，我们把重点放在并行子目标中的两个方面： 1-新的分析工具适用于在非线性参数场、切换混合策略和强加拓扑的困难背景下的生物运动系统。过去在NSERC的资助下，我们产生了现在得到NSERC/I2I支持的软件创新：创建交换系统中响应模式的自动分类和识别工具，以评估每种模式的动态，而不考虑交换特性。它们的目标是分发给该领域的神经科学家、神经耳科医生和临床医生。 2-基于脑干和脊髓神经回路的特殊性质(拓扑)的眼头和眼反射生物控制策略的新模型(由CIHR资助)。这些模型改变了对行为和神经层面数据的解释，并提出了新的方案，以测试在更自然的混合感觉环境中运动系统的完整性，如在日常生活中。这现在才有可能，因为我们的算法可以处理不同的情况。最近，对手臂伸展的探索(由NSERC资助)预测，我们在眼头协调中使用的控制策略与灵长类手臂或腿的轨迹以及脊髓的拓扑结构一致。其结果是控制比通常在机器人文献中使用的要简单得多--即无需规划的移动。 所有这些系统都使用类似的网络拓扑结构，在空间组织和感知器融合的使用方面。这提出了一种可能的通用控制理论，适用于所有平台，无论是堆叠和旋转的平台，还是节段式肢体。因此，我们的长期目标是： 形式化一种用于移动系统的仿生控制策略，该策略将允许执行简单任务而无需先验轨迹规划 作为短期目标，将对生物学中发现的上述特征进行评估，并将其形式化，以供一般应用，包括： ·类似于脑干和脊髓中神经连接的控制器结构或拓扑(对称性，传感器-运动相互作用的位置)，以通过模式选择标准(开关、映射矩阵的顺序)首先在空间上1D，然后3D地嵌入动态模式。 ·传感器与其目标平台之间以及平台之间的最佳非线性增益场，它们决定了轨迹的动力学和曲率。 ·通过执行中央控制(模拟大脑皮层和小脑)进行控制器“调整”的学习策略，并允许独立调整运动速度和轨迹，而无需重新计算轨迹计划。 主要假设是：任务错误直接发送到所有参与平台(没有单独的目标)；每个分段和端点的轨迹作为网络动态的属性演变，而不是像经典机器人学中那样通过计算预先施加。如果能针对不同的机械系统进行推广和调整，将会在几个领域产生影响：试图驱动瘫痪肢体的界面会有一种自然的感觉；如果交替肌肉的输入能够保持假肢的自然激活模式，那么使用假肢的学习曲线会更快；以及一个更自然的界面系统，用于远程设备操作。目标是让用户保持他/她的习惯性激活模式，而不是开发可能与他们的神经能力相冲突的新模式。最后，这应该会导致智能机器人系统以较低的计算需求自主适应环境。

Our long-term objectives are to learn from biological movement and devise efficient and novel control strategies for artificial systems (robots, prostheses) and for assisted human control (FES, neural interfaces). To date, we have focused on two streams in parallel sub-objectives: 1- New analytical tools applicable to biological movement systems in the difficult context of non-linear parametric fields, switched hybrid strategies and imposed topologies. Funded by NSERC in the past, we produced software innovations now supported by NSERC/ I2I: to create automated classification of response modes in switched systems and identification tools to estimate the dynamics of each mode despite switching characteristics. They are targeted for distribution to Neuroscientists, Neuro-otologists and Clinicians in the field. 2- Novel models of biological control strategies for eye-head and ocular reflexes (funded by CIHR) based on the special nature (topology) of neural circuits in the brainstem and spinal cord. These models change the interpretation of data at both behavioural and neural levels, and suggest new protocols to test the integrity of motor systems in more natural mixed-sensory environments, as in daily life. This is only possible now because our algorithms can handle diverse conditions. More recently, explorations on arm reaching (funded by NSERC) predict that our control strategies used in eye-head coordination are consistent with primate arm or leg trajectories and the topology of the spinal cord. The result is much simpler control than typically used in the robotic literature – i.e. movement without planning. All of these systems use network topologies similar in their spatial organization and in the use of sensorimotor fusion. This suggests a possible general control theory for all platforms, be they stacked and rotatory, or segmental limbs. So the long-term goal is: TO FORMALIZE A BIOMIMETIC CONTROL STRATEGY FOR MOVING SYSTEMS THAT WILL ALLOW EXECUTION OF SIMPLE TASKS WITHOUT A-PRIORI TRAJECTORY PLANNING As short term-objectives, the mentioned characteristics found in biology will be evaluated and formalized for general applications, with: • A controller structure or topology analoguous to neural connections in the brainstem and spinal cord (symmetry, sites of sensor-motor interactions) to imbed dynamic modes with mode selection criteria (switch, order of mapping matrix) first spatially 1D then 3D. • Optimal Non-linear gain fields between sensors and their target platforms, and between platforms, which determine the dynamics and curvature of trajectories. • Learning strategies for controller “tuning” by executive central control (analogs cortex and cerebellum) and allow independent adjustments of movement speed and trajectory, without the need to re-compute a trajectory plan. The main assumptions are: task error sent directly to all participating platforms (no separate goals); trajectories for each segment and the end-point evolve as a property of the dynamics of the network, rather than pre-imposed by computation as in classical robotics. If this can be generalized and easily tuned for different mechanical systems, it would have impact in several areas: a 'natural feel' for interfaces that attempt to drive paralyzed limbs, a faster learning curve for the use of artificial limbs if the input from alternate muscles can preserve their natural activation patterns, and a more natural interface system for remote device operation. The goal is to allow the user to keep his/her habitual activation patterns, rather than develop new ones that may be in conflict with their neural capabilities. Finally, this should lead to smart robotic systems that adjust to contexts autonomously, with low computational demand.