The Effects of the Environment and Linear Actuators on Robot Morphologies

• URL: http://arxiv.org/abs/2204.00934v3
• Date: Fri, 26 Aug 2022 15:07:12 GMT
• Title: The Effects of the Environment and Linear Actuators on Robot Morphologies
• Authors: Steven Oud and Koen van der Pool
• Abstract summary: We study the effect of adding a new module inspired by the skeletal muscle to the existing RoboGen framework: the linear actuator. We find significant differences in the morphologies of robots evolved in a plain environment and robots evolved in a rough environment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of evolutionary robotics uses principles of natural evolution to design robots. In this paper, we study the effect of adding a new module inspired by the skeletal muscle to the existing RoboGen framework: the linear actuator. Additionally, we investigate how robots evolved in a plain environment differ from robots evolved in a rough environment. We consider the task of directed locomotion for comparing evolved robot morphologies. The results show that the addition of the linear actuator does not have a significant impact on the performance and morphologies of robots evolved in a plain environment. However, we find significant differences in the morphologies of robots evolved in a plain environment and robots evolved in a rough environment. We find that more complex behavior and morphologies emerge when we change the terrain of the environment.

Related papers

• Body Transformer: Leveraging Robot Embodiment for Policy Learning [51.531793239586165]
  Body Transformer (BoT) is an architecture that leverages the robot embodiment by providing an inductive bias that guides the learning process. We represent the robot body as a graph of sensors and actuators, and rely on masked attention to pool information throughout the architecture. The resulting architecture outperforms the vanilla transformer, as well as the classical multilayer perceptron, in terms of task completion, scaling properties, and computational efficiency.
  arXiv  Detail & Related papers  (2024-08-12T17:31:28Z)
• Evolution and learning in differentiable robots [0.0]
  We use differentiable simulations to rapidly and simultaneously optimize individual neural control of behavior across a large population of candidate body plans. Non-differentiable changes to the mechanical structure of each robot in the population were applied by a genetic algorithm in an outer loop of search. One of the highly differentiable morphologies discovered in simulation was realized as a physical robot and shown to retain its optimized behavior.
  arXiv  Detail & Related papers  (2024-05-23T15:45:43Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• 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)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer [57.045140028275036]
  We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
  arXiv  Detail & Related papers  (2022-02-10T18:50:25Z)
• Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots [29.02903745467536]
  We propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. We develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques.
  arXiv  Detail & Related papers  (2022-01-24T18:39:22Z)
• Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots [71.61319876928009]
  We show that learning to minimize energy consumption plays a key role in the emergence of natural locomotion gaits at different speeds in real quadruped robots. The emergent gaits are structured in ideal terrains and look similar to that of horses and sheep. The same approach leads to unstructured gaits in rough terrains which is consistent with the findings in animal motor control.
  arXiv  Detail & Related papers  (2021-10-25T17:59:58Z)
• The Dynamic of Body and Brain Co-Evolution [0.0]
  We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies.
  arXiv  Detail & Related papers  (2020-11-23T14:41:57Z)
• Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution [5.000272778136268]
  We propose to design a developmental process happening at the phylogenetic timescale. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots. Our method is conceptually simple, and can be effective on small or large populations of robots.
  arXiv  Detail & Related papers  (2020-10-28T11:24:23Z)
• Environmental Adaptation of Robot Morphology and Control through Real-world Evolution [5.08706161686979]
  We apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology.
  arXiv  Detail & Related papers  (2020-03-30T07:57:19Z)

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.