Open-endedness induced through a predator-prey scenario using modular robots

• URL: http://arxiv.org/abs/2309.11275v1
• Date: Wed, 20 Sep 2023 12:58:51 GMT
• Title: Open-endedness induced through a predator-prey scenario using modular robots
• Authors: Dimitri Kachler and Karine Miras
• Abstract summary: This work investigates how a predator-prey scenario can induce the emergence of Open-Ended Evolution (OEE) We utilize modular robots of fixed morphologies whose controllers are subject to evolution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates how a predator-prey scenario can induce the emergence of Open-Ended Evolution (OEE). We utilize modular robots of fixed morphologies whose controllers are subject to evolution. In both species, robots can send and receive signals and perceive the relative positions of other robots in the environment. Specifically, we introduce a feature we call a tagging system: it modifies how individuals can perceive each other and is expected to increase behavioral complexity. Our results show the emergence of adaptive strategies, demonstrating the viability of inducing OEE through predator-prey dynamics using modular robots. Such emergence, nevertheless, seemed to depend on conditioning reproduction to an explicit behavioral criterion.

Related papers

• No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots [0.24554686192257422]
  We show that intelligent behavior can be created without a separate and explicit brain for robot control. Specifically, we show that adaptive and complex behavior can be created in voxel-based virtual soft robots by using simple reactive materials.
  arXiv  Detail & Related papers  (2024-07-23T16:20:36Z)
• Innate Motivation for Robot Swarms by Minimizing Surprise: From Simple Simulations to Real-World Experiments [6.21540494241516]
  Large-scale mobile multi-robot systems can be beneficial over monolithic robots because of higher potential for robustness and scalability. Developing controllers for multi-robot systems is challenging because the multitude of interactions is hard to anticipate and difficult to model. Innate motivation tries to avoid the specific formulation of rewards and work instead with different drivers, such as curiosity. A unique advantage of the swarm robot case is that swarm members populate the robot's environment and can trigger more active behaviors in a self-referential loop.
  arXiv  Detail & Related papers  (2024-05-04T06:25:58Z)
• Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment [92.48012013825988]
  We study the problem of adapting on-the-fly to novel scenarios during deployment. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors. We demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped.
  arXiv  Detail & Related papers  (2023-11-02T08:22:28Z)
• Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
  We present a fully learning-based approach for real-world humanoid locomotion. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
  arXiv  Detail & Related papers  (2023-03-06T18:59:09Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• The Effects of the Environment and Linear Actuators on Robot Morphologies [0.0]
  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.
  arXiv  Detail & Related papers  (2022-04-02T20:03:32Z)
• Collective motion emerging from evolving swarm controllers in different environments using gradient following task [2.7402733069181]
  We consider a challenging task where robots with limited sensing and communication abilities must follow the gradient of an environmental feature. We use Differential Evolution to evolve a neural network controller for simulated Thymio II robots. Experiments confirm the feasibility of our approach, the evolved robot controllers induced swarm behaviour that solved the task.
  arXiv  Detail & Related papers  (2022-03-22T10:08:50Z)
• 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)
• Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment [1.4502611532302039]
  We show how changing the environment, where the agent locomotes, affects the convergence of morphologies. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments.
  arXiv  Detail & Related papers  (2021-04-07T11:28:23Z)
• Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots [58.720142291102135]
  Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus. The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
  arXiv  Detail & Related papers  (2021-01-24T12:44:38Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

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.