MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics

12/08/2020
by   Jørgen Nordmoen, et al.
0

In modular robotics, modules can be reconfigured to change the morphology of the robot, making it able to adapt for specific tasks. However, optimizing both the body and control is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize morphologies in modular robotics. We compare two objective-based search algorithms, with MAP-Elites. To understand the benefit of diversity we transition the evolved populations into two difficult environments to see if diversity can have an impact on solving complex environments. In addition, we analyse the genealogical ancestry to shed light on the notion of stepping stones as key to enable high performance. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. For the transition between environments the results show that MAP-Elites is better at regaining performance by promoting morphological diversity. With the analysis of genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the other objective-based search algorithms. Transitioning the populations to more difficult environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. The paper shows the advantage of promoting diversity for solving a locomotion task in different environments for modular robotics. By showing that the quality and diversity of stepping stones in Evolutionary Algorithms is an important factor for overall performance we have opened up a new area of analysis and results.

READ FULL TEXT

page 1

page 4

page 8

page 9

page 10

page 11

page 12

page 13

research
08/05/2020

Quality and Diversity in Evolutionary Modular Robotics

In Evolutionary Robotics a population of solutions is evolved to optimiz...
research
04/25/2021

Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots

Designing optimal soft modular robots is difficult, due to non-trivial i...
research
04/07/2021

Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment

Designing robots by hand can be costly and time consuming, especially if...
research
10/21/2021

Heritability in Morphological Robot Evolution

In the field of evolutionary robotics, choosing the correct encoding is ...
research
03/07/2023

MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy

Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolut...
research
04/24/2019

Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a p...
research
03/19/2021

Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring

One of the most important lessons from the success of deep learning is t...

Please sign up or login with your details

Forgot password? Click here to reset