Towards biomimetic control of robotic or paralyzed limbs

我们的长期目标是从生物运动中学习，并为人工系统（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。迄今为止，我们专注于并行子目标的两个流：1-适用于非线性参数场，切换混合策略和强加拓扑的困难背景下的生物运动系统的新分析工具。过去由NSERC资助，我们生产了软件创新，现在由NSERC/ I2I支持：在切换系统中创建响应模式的自动分类和识别工具，以估计每种模式的动态，尽管切换特性。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（拓扑结构）的眼-头和眼反射生物控制策略新模型（CIHR资助）。这些模型在行为和神经层面上改变了对数据的解释，并提出了在更自然的混合感觉环境（如日常生活）中测试运动系统完整性的新方案。现在这才成为可能，因为我们的算法可以处理各种情况。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动物手臂或腿的轨迹和脊髓的拓扑结构一致。其结果是比机器人文献中通常使用的控制简单得多-即没有计划的运动。所有这些系统在空间组织和感觉运动融合方面都使用了相似的网络拓扑结构。这暗示了一种可能适用于所有平台的通用控制理论，无论它们是堆叠的、旋转的还是分段的肢体。所以我们的长期目标是：将移动系统的仿生控制策略形式化，使其能够在没有先验轨迹规划的情况下执行简单任务作为短期目标，将对生物学中发现的上述特征进行评估并形式化，用于一般应用，包括：•控制器结构或拓扑结构类似于脑干和脊髓中的神经连接（对称性，传感器-运动相互作用的位置），以嵌入具有模式选择标准的动态模式(开关，映射矩阵的顺序)首先是空间1D，然后是3D。•传感器与其目标平台之间以及平台之间的最优非线性增益场，决定了轨迹的动力学和曲率。•通过执行中央控制（类似皮层和小脑）学习控制器“调谐”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）；每个部分和终点的轨迹都是作为网络动力学的属性而进化的，而不是像经典机器人那样通过计算预先强加的。如果这种方法可以推广到不同的机械系统中，那么它将在几个方面产生影响：尝试驱动瘫痪肢体的界面的“自然感觉”，如果来自替代肌肉的输入可以保持其自然激活模式，那么使用假肢的学习曲线将更快，远程设备操作的界面系统将更自然。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，这将导致智能机器人系统自主适应环境，计算需求低。

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 PLANNINGAs 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.