Tommasino Paolo, Campolo Domenico
Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore.
Bioinspir Biomim. 2017 Feb 3;12(2):026003. doi: 10.1088/1748-3190/aa5558.
In this work, we address human-like motor planning in redundant manipulators. Specifically, we want to capture postural synergies such as Donders' law, experimentally observed in humans during kinematically redundant tasks, and infer a minimal set of parameters to implement similar postural synergies in a kinematic model. For the model itself, although the focus of this paper is to solve redundancy by implementing postural strategies derived from experimental data, we also want to ensure that such postural control strategies do not interfere with other possible forms of motion control (in the task-space), i.e. solving the posture/movement problem. The redundancy problem is framed as a constrained optimization problem, traditionally solved via the method of Lagrange multipliers. The posture/movement problem can be tackled via the separation principle which, derived from experimental evidence, posits that the brain processes static torques (i.e. posture-dependent, such as gravitational torques) separately from dynamic torques (i.e. velocity-dependent). The separation principle has traditionally been applied at a joint torque level. Our main contribution is to apply the separation principle to Lagrange multipliers, which act as task-space force fields, leading to a task-space separation principle. In this way, we can separate postural control (implementing Donders' law) from various types of tasks-space movement planners. As an example, the proposed framework is applied to the (redundant) task of pointing with the human wrist. Nonlinear inverse optimization (NIO) is used to fit the model parameters and to capture motor strategies displayed by six human subjects during pointing tasks. The novelty of our NIO approach is that (i) the fitted motor strategy, rather than raw data, is used to filter and down-sample human behaviours; (ii) our framework is used to efficiently simulate model behaviour iteratively, until it converges towards the experimental human strategies.
在这项工作中，我们研究了冗余机器人的类人运动规划。具体而言，我们希望捕捉诸如东德斯定律之类的姿势协同效应，这是在人类进行运动学冗余任务时通过实验观察到的，并推断出一组最小参数，以便在运动学模型中实现类似的姿势协同效应。对于模型本身，尽管本文的重点是通过实施从实验数据得出的姿势策略来解决冗余问题，但我们还希望确保此类姿势控制策略不会干扰其他可能的运动控制形式（在任务空间中），即解决姿势/运动问题。冗余问题被构建为一个约束优化问题，传统上通过拉格朗日乘数法求解。姿势/运动问题可以通过分离原理来解决，该原理基于实验证据，假定大脑分别处理静态扭矩（即与姿势相关的，如重力扭矩）和动态扭矩（即与速度相关的）。分离原理传统上是在关节扭矩层面应用的。我们的主要贡献是将分离原理应用于拉格朗日乘数，拉格朗日乘数充当任务空间力场，从而得出任务空间分离原理。通过这种方式，我们可以将姿势控制（实施东德斯定律）与各种类型的任务空间运动规划器分开。例如，所提出的框架被应用于人类手腕指向的（冗余）任务。非线性逆优化（NIO）用于拟合模型参数，并捕捉六名人类受试者在指向任务期间展示的运动策略。我们的NIO方法的新颖之处在于：（i）使用拟合的运动策略而非原始数据来过滤和下采样人类行为；（ii）我们的框架用于高效地迭代模拟模型行为，直到它收敛到实验性的人类策略。
