Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation

• URL: http://arxiv.org/abs/2309.00241v1
• Date: Fri, 1 Sep 2023 04:16:23 GMT
• Title: Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation
• Authors: Vahid Pashaei Rad, Vahid Azimi Rad, Saleh Valizadeh Sotubadi
• Abstract summary: Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of multiple squares of the same size and the robot only observes the neighboring squares of its current square. It should be stated that the robot only moves either up and down or right and left. The environment returns feedback to the learning automata to optimize its decision making in the next steps resulting in cellular automata training. Simultaneously a spiking neural network is trained to implement long term improvements and reductions on the paths. The results show that the integration of both cellular automata and spiking neural network ends up in reinforcing the proper paths and training time reduction at the same time.

Related papers

• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• Game of Intelligent Life [0.0]
  Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines.
  arXiv  Detail & Related papers  (2023-01-02T23:06:26Z)
• Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot [20.813028212068424]
  We study different techniques that allow adapting an object segmentation model in presence of novel objects or different domains. We propose a pipeline for fast instance segmentation learning for robotic applications where data come in stream. We benchmark the proposed pipeline on two datasets and we deploy it on a real robot, iCub humanoid.
  arXiv  Detail & Related papers  (2022-06-27T17:14:04Z)
• Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications [59.34642007625687]
  The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots. An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
  arXiv  Detail & Related papers  (2022-05-03T17:14:47Z)
• Learning Time-optimized Path Tracking with or without Sensory Feedback [5.254093731341154]
  We present a learning-based approach that allows a robot to quickly follow a reference path defined in joint space. The robot is controlled by a neural network that is trained via reinforcement learning using data generated by a physics simulator.
  arXiv  Detail & Related papers  (2022-03-03T19:13:31Z)
• 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)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• 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)

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.