Evolutionary Robotics: From Simulation-Based Behavior Learning to Direct Teaching in Real Environments

2009 ◽  
pp. 69-96
Author(s):  
Seiji Yamada ◽  
Daisuke Katagami
Keyword(s):  
Evolutionary Robotics ◽  
Simulation Based ◽  
Behavior Learning ◽  
Direct Teaching

scholarly journals Open Issues in Evolutionary Robotics

2016 ◽  
Vol 24 (2) ◽  
pp. 205-236 ◽  
Author(s):  
Fernando Silva ◽  
Miguel Duarte ◽  
Luís Correia ◽  
Sancho Moura Oliveira ◽  
Anders Lyhne Christensen
Keyword(s):  
Evolutionary Computation ◽  
Autonomous Robots ◽  
Behavioral Control ◽  
Evolutionary Robotics ◽  
Simulation Based ◽  
Canonical Approach ◽  
Task Specification ◽  
Open Issues

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

scholarly journals LCrowdV: Generating Labeled Videos for Simulation-Based Crowd Behavior Learning

2016 ◽  
pp. 709-727 ◽  
Author(s):  
Ernest Cheung ◽  
Tsan Kwong Wong ◽  
Aniket Bera ◽  
Xiaogang Wang ◽  
Dinesh Manocha
Keyword(s):  
Crowd Behavior ◽  
Simulation Based ◽  
Behavior Learning

Author response for "A complex garment assembly line balancing using simulation‐based optimization"

2020 ◽  
Author(s):  
Ocident Bongomin ◽  
Josphat Igadwa Mwasiagi ◽  
Eric Oyondi Nganyi ◽  
Ildephonse Nibikora
Keyword(s):  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Author Response ◽  
Simulation Based ◽  
Simulation Based Optimization

Inquiry Learning

2009 ◽  
Vol 23 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Astrid Wichmann ◽  
Detlev Leutner
Keyword(s):  
Scientific Reasoning ◽  
Inquiry Learning ◽  
Instructional Support ◽  
Test Results ◽  
Knowledge Test ◽  
Simulation Based ◽  
Scientific Experiments ◽  
Specific Support ◽  
Science Classes

Seventy-nine students from three science classes conducted simulation-based scientific experiments. They received one of three kinds of instructional support in order to encourage scientific reasoning during inquiry learning: (1) basic inquiry support, (2) advanced inquiry support including explanation prompts, or (3) advanced inquiry support including explanation prompts and regulation prompts. Knowledge test as well as application test results show that students with regulation prompts significantly outperformed students with explanation prompts (knowledge: d = 0.65; application: d = 0.80) and students with basic inquiry support only (knowledge: d = 0.57; application: d = 0.83). The results are in line with a theoretical focus on inquiry learning according to which students need specific support with respect to the regulation of scientific reasoning when developing explanations during experimentation activities.

Concepts—Behavior—Learning

1960 ◽  
Vol 5 (5) ◽  
pp. 145-146
Author(s):  
ERNEST R. HILGARD
Keyword(s):  
Behavior Learning
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