Centralized and Decentralized Control in Modular Robots and Their Effect on Morphology

• URL: http://arxiv.org/abs/2206.13366v1
• Date: Mon, 27 Jun 2022 15:22:46 GMT
• Title: Centralized and Decentralized Control in Modular Robots and Their Effect on Morphology
• Authors: Mia-Katrin Kvalsund, Kyrre Glette, Frank Veenstra
• Abstract summary: We study the effects of centralized and decentralized controllers on modular robot performance and morphologies. A decentralized approach that was more independent of morphology size performed significantly better than the other approaches.
• Score: 1.4502611532302039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Evolutionary Robotics, evolutionary algorithms are used to co-optimize morphology and control. However, co-optimizing leads to different challenges: How do you optimize a controller for a body that often changes its number of inputs and outputs? Researchers must then make some choice between centralized or decentralized control. In this article, we study the effects of centralized and decentralized controllers on modular robot performance and morphologies. This is done by implementing one centralized and two decentralized continuous time recurrent neural network controllers, as well as a sine wave controller for a baseline. We found that a decentralized approach that was more independent of morphology size performed significantly better than the other approaches. It also worked well in a larger variety of morphology sizes. In addition, we highlighted the difficulties of implementing centralized control for a changing morphology, and saw that our centralized controller struggled more with early convergence than the other approaches. Our findings indicate that duplicated decentralized networks are beneficial when evolving both the morphology and control of modular robots. Overall, if these findings translate to other robot systems, our results and issues encountered can help future researchers make a choice of control method when co-optimizing morphology and control.

Related papers

• Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks [11.194306044434502]
  Decentralized controllers struggle to maintain flock cohesion. graph neural network (GNN) architecture has emerged as indispensable machine learning tool for developing decentralized controllers. We show that our symmetry-aware controller generalizes better than existing GNN controllers.
  arXiv  Detail & Related papers  (2025-02-24T19:59:37Z)
• A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies [2.884244918665901]
  What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? We perform an experimental comparison of three controller-and-learner combinations. We compare their efficacy, efficiency, and robustness.
  arXiv  Detail & Related papers  (2023-09-25T07:11:43Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)
• Co-evolving morphology and control of soft robots using a single genome [0.0]
  We present a new method to co-evolve morphology and control of robots. Our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster.
  arXiv  Detail & Related papers  (2022-12-22T07:34:31Z)
• Low-Rank Modular Reinforcement Learning via Muscle Synergy [25.120547719120765]
  Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. We propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control.
  arXiv  Detail & Related papers  (2022-10-26T16:01:31Z)
• Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection [101.38634057635373]
  We focus on applications where robots are required to jointly select actions to maximize team submodular objectives. We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
  arXiv  Detail & Related papers  (2021-05-18T15:32:07Z)
• Decentralized Control with Graph Neural Networks [147.84766857793247]
  We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
  arXiv  Detail & Related papers  (2020-12-29T18:59:14Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)
• Graph Neural Networks for Decentralized Controllers [171.6642679604005]
  Dynamical systems comprised of autonomous agents arise in many relevant problems such as robotics, smart grids, or smart cities. Optimal centralized controllers are readily available but face limitations in terms of scalability and practical implementation. We propose a framework using graph neural networks (GNNs) to learn decentralized controllers from data.
  arXiv  Detail & Related papers  (2020-03-23T13:51:18Z)
• VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms [237.25930757584047]
  We propose to learn decentralized controllers based on solely raw visual inputs. For the first time, that integrates the learning of two key components: communication and visual perception. Our proposed learning framework combines a convolutional neural network (CNN) for each robot to extract messages from the visual inputs, and a graph neural network (GNN) over the entire swarm to transmit, receive and process these messages.
  arXiv  Detail & Related papers  (2020-02-06T15:25:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.