Online sensorimotor learning and adaptation for inverse dynamics control
Online sensorimotor learning and adaptation for inverse dynamics control
复制标题
逆动态控制的在线感觉运动学习和适应
DOI：
10.1016/j.neunet.2021.06.029
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
P. Manoonpong
中科院分区：
文献类型：
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作者：
Xiaofeng Xiong;P. Manoonpong
本文提出了一种用于仿人手臂逆动力学控制的微数据（< 10次试验）感觉运动学习和适应（SEED）模型。SEED模型由前馈高斯运动基元（GATE）神经网络和自适应反馈阻抗（AIM）机制组成。在GATE网络中学习试验中的感觉运动权重，而AIM机制用于在线调整试验中的阻抗增益。该模型通过两关节机器人手臂上的周期性和非周期性跟踪任务进行了验证。因此，与最先进的深度学习所需的数千次试验相比，该模型使手臂能够在10次试验内稳定地学习任务。该模型有利于探索未知的手臂动力学，其中手肘关节需要少得多的主动控制相比，肩膀。这种控制低于总工作量的3%。该发现符合人体手臂控制中的近端-远端控制梯度。总而言之，提出的SEED模型为实现数据高效的感觉运动学习和类似人类手臂运动的适应铺平了道路。
We propose a micro-data (< 10 trials) sensorimotor learning and adaptation (SEED) model for human-like arm inverse dynamics control. The SEED model consists of a feedforward Gaussian motor primitive (GATE) neural network and an adaptive feedback impedance (AIM) mechanism. Sensorimotor weights over trials are learned in the GATE network, while the AIM mechanism is used to online tune impedance gains in a trial. The model was validated by periodic and non-periodic tracking tasks on a two-joint robot arm. As a result, the proposed model enables the arm to stably learn the tasks within 10 trials, compared to thousands of trials required by state-of-art deep learning. This model facilitates the exploration of unknown arm dynamics, in which the elbow joint requires much less active control compared to the shoulder. This control goes below 3% of the overall effort. This finding complies with a proximal–distal control gradient in human arm control. Taken together, the proposed SEED model paves a way for implementing data-efficient sensorimotor learning and adaptation of human-like arm movement.