Evolving Multi-Objective Neural Network Controllers for Robot Swarms

• URL: http://arxiv.org/abs/2307.14237v1
• Date: Wed, 26 Jul 2023 15:05:17 GMT
• Title: Evolving Multi-Objective Neural Network Controllers for Robot Swarms
• Authors: Karl Mason, Sabine Hauert
• Abstract summary: This research proposes a multi-objective evolutionary neural network approach to developing controllers for swarms of robots. The swarm robot controllers are trained in a low-fidelity Python simulator and then tested in a high-fidelity simulated environment using Webots.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many swarm robotics tasks consist of multiple conflicting objectives. This research proposes a multi-objective evolutionary neural network approach to developing controllers for swarms of robots. The swarm robot controllers are trained in a low-fidelity Python simulator and then tested in a high-fidelity simulated environment using Webots. Simulations are then conducted to test the scalability of the evolved multi-objective robot controllers to environments with a larger number of robots. The results presented demonstrate that the proposed approach can effectively control each of the robots. The robot swarm exhibits different behaviours as the weighting for each objective is adjusted. The results also confirm that multi-objective neural network controllers evolved in a low-fidelity simulator can be transferred to high-fidelity simulated environments and that the controllers can scale to environments with a larger number of robots without further retraining needed.

Related papers

• 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)
• RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis [102.1876259853457]
  We propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints. To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative self-updating methodology is introduced for supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-02-25T15:31:43Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
  We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots. We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
  arXiv  Detail & Related papers  (2022-09-12T15:14:32Z)
• 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)
• Malleable Agents for Re-Configurable Robotic Manipulators [0.0]
  We propose an RL agent with sequence neural networks embedded in the deep neural network to adapt to robotic arms with varying number of links. With the additional tool of domain randomization, this agent adapts to different configurations with varying number/length of links and dynamics noise.
  arXiv  Detail & Related papers  (2022-02-04T21:22:00Z)
• 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)
• Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction [0.2549905572365809]
  We have proposed an Inception module based Convolutional Neural Network Architecture. The model is implemented in a humanoid robot, NAO in real time and robustness of the model is evaluated.
  arXiv  Detail & Related papers  (2020-01-12T05:25:02Z)

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.