Learning and Control Model of the Arm for Loading

Kyoungsik KIM  Hiroyuki KAMBARA  Duk SHIN  Yasuharu KOIKE 

Publication
IEICE TRANSACTIONS on Information and Systems  Vol.E92-D  No.4  pp.705-716
Publication Date: 2009/04/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Biocybernetics, Neurocomputing
Keyword: 
motor control,  FDM,  loading,  actor-critic,  feedback-error-learning, 

Full Text: PDF(825.2KB)

Summary: 
We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.