How is somatosensory information used to adapt to changes in the mechanical environment?

Recent studies examining adaptation to unexpected changes in the mechanical environment highlight the use of position error in the adaptation process. However, force information is also available. In this chapter, we examine adaptation processes in three separate studies where the mechanical environment was changed intermittently. We compare the expected consequences of using position error and force information in the changes to motor commands following a change in the mechanical environment. In general, our results support the use of position error over force information and are consistent with current computational models of motor learning. However, in situations where the change in the mechanical environment eliminates position error the central nervous system does not necessarily respond as would be predicted by these models. We suggest that it is necessary to take into account the statistics of prior experience to account for our observations. Another deficiency in these models is the absence of a mechanism for modulating limb mechanical impedance during adaptation. We propose a relatively simple computational model based on reflex responses to perturbations which is capable of accounting for iterative changes in temporal patterns of muscle co-activation.

Single trial learning of external dynamics: What can the brain teach us about learning mechanisms?

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Learning feedforward commands to muscles using time-shifted sensory feedback

In adapting to changing forces in the mechanical environment, humans change the force being applied by the limb by reciprocal changes in the activation of antagonistic muscles. However, they also cocontract these muscles when interaction with the environment is mechanically unstable to increase the mechanical impedance of the limb. We have postulated that appropriate patterns of muscle activation could be learned using a simple scheme in which the naturally occurring stretch reflex is used as a template to adjust feedforward commands to muscles. Feedforward commands are modified iteratively by shifting a scaled version of the reflex response forward in time and adding it to the previous feedforward command. We show that such an algorithm can account for the principal features of changes in muscle activation observed when human subjects adapt to instabilities in the mechanical environment. © 2006.

How are internal models of unstable tasks formed?

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

Measuring stiffness during arm movements in various dynamic environments

The technique presented in this paper enables a simple, accurate and unbiased measurement of hand stiffness during human arm movements. Using a computer-controlled mechanical interface, the hand is shifted relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the restoring force divided by the position amplitude of the perturbation. A precise prediction algorithm insures the measurement quality. We used this technique to measure stiffness in free movements and after adaptation to a linear velocity dependent force field. The subjects compensated for the external force by co-contracting muscles selectively. The stiffness geometry changed with learning and stiffness tended to increase in the direction of the external force.

模态逻辑公式为见证的互模拟等价判定

判定两个进程是否具有某种等价关系，是形式化验证的重要组成部分，很多种等价关系被定义出来以满足不同的验证需求，强互模拟等价和分支互模拟等价是其中两个重要的两种等价关系。强互模拟等价可以用HML(Hennessy-Milner Logic)逻辑刻画，分支互模 拟等价可以用HMLU(HML with Until)等逻辑刻画。两个模型不能强互模拟等价或者分支互模拟等价时，存在一个作为见证的相应逻辑描述的公式，该公式只能被其中一个模型满足，称之为诊断公式。诊断公式的意义在于有助于分析理解模型为什么不具有相应的等价关系，给出诊断公式是对判定算法有意义的扩充。修改后的PT(Paige-Tarjan)算法可以用于计算有限状态进程之间的强互模 拟等价关系，修改后的GV(Groote-Vaandrager)算法可以用于计算分支互模拟，对它们相应的扩充可以为不能强互模拟等价或分支互模拟等价的进程生成诊断公式。 主要工作如下：分析为强互模拟等价和分支互模拟等价生成诊断公式的方法。设计处理有限状态进程表达式并将其转化为标号迁移系统的算法。扩展PT 算法，使其适用于标号迁移系统，并在判定强互模拟等价的过程中为不能强互模拟的进程生成HML 描述的诊断公式。扩展GV 算法，使其能够在判定标号迁移系统上分支互模拟的同时，为不能分支互模拟的进程生成HMLU 描述的诊断公式。