Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

Clicks: 285
ID: 261377
2020
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Abstract
We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: “hole search” and “peg insertion”. The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally.
Reference Key
cho2020appliedlearning, Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nam Jun Cho;Sang Hyoung Lee;Jong Bok Kim;Il Hong Suh;Cho, Nam Jun;Lee, Sang Hyoung;Kim, Jong Bok;Suh, Il Hong;
Journal applied sciences
Year 2020
DOI
10.3390/app10082719
URL
https://www.mdpi.com/2076-3417/10/8/2719
Keywords
reinforcement learning robot hidden markov model peg-in-hole task dynamic movement primitive motor skill

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