CAJ | 학술논문

该文在 Hoff-Arbib 最小加加速度(minimum jerk)控制理论的基础上，提出一种具有生物学意义的小脑控制模型，用以解释延伸和抓取过程中，手臂运动与预抓取之间时间协调的问题。在对这种模型如何学习超前状态和剩余时间(TTG)预测两个关键功能进行讨论的基础上，重点对2维空间的延伸与抓取运动进行了仿真。结果表明，由 Hoff-Arbib 模型所获得的有关延伸与抓取运动的动力学的一些关键特征，小脑控制模型也能实现，有些性能甚至更好。总之，通过训练和学习，模型能获得精确平滑的运动轨迹。
해문재 Hoff-Arbib 최소가가속도(minimum jerk)공제이론적기출상，제출일충구유생물학의의적소뇌공제모형，용이해석연신화조취과정중，수비운동여예조취지간시간협조적문제。재대저충모형여하학습초전상태화잉여시간(TTG)예측량개관건공능진행토론적기출상，중점대2유공간적연신여조취운동진행료방진。결과표명，유 Hoff-Arbib 모형소획득적유관연신여조취운동적동역학적일사관건특정，소뇌공제모형야능실현，유사성능심지경호。총지，통과훈련화학습，모형능획득정학평활적운동궤적。
Based on Hoff and Arbib’ s control theory of the minimum jerk, this paper presents a new control model with cerebellar-like structure which is able to account for the temporal coordination of arm transport and hand preshape during reach and grasp tasks. And it is suggested that how the structure can learn the two key functions required in the Hoff-Arbib theory, namely state look-ahead and Time-To-Go (TTG) estimation. By the simulation for two-dimensional motion of arm transport and hand preshape, the results demonstrate that some key features of human reach-grasp kinematics obtained by Hoff-Arbib model can be achieved by the cerebellum control model and some performances are even better. In a word, by learning and training, this model can create a more accurate and smooth motor trajectory.