A Conformal Mapping-based Framework for Robot-to-Robot and Sim-to-Real Transfer Learning

被引：1
|
作者：
Gao, Shijie [1 ,2 ]
Bezzo, Nicola [1 ,2 ]
机构：
[1] Univ Virginia, Dept Elect & Comp Engn, Charlottesville, VA 22904 USA
[2] Univ Virginia, Link Lab, Charlottesville, VA 22904 USA
来源：
2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2021年
关键词：
ALGORITHM;
D O I：
10.1109/IROS51168.2021.9636682
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz-Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible with the learner's capabilities. Our approach is validated with simulations and experiments with different robotic systems navigating occluding environments.
引用
收藏
页码：1289 / 1295
页数：7
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