An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi

• URL: http://arxiv.org/abs/2006.04765v1
• Date: Mon, 8 Jun 2020 17:35:48 GMT
• Title: An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi
• Authors: Ioannis Polykretis, Konstantinos P. Michmizos
• Abstract summary: Locomotion is a crucial challenge for legged robots that is addressed "effortlessly" by biological networks abundant in nature, named central pattern generators (CPG) Here, we propose a brain-morphic CPG controler based on a comprehensive spiking neural-astrocytic network that generates two gait patterns for a hexapod robot. Our results pave the way for scaling this and other approaches towards Loihi-controlled locomotion in autonomous mobile robots.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Locomotion is a crucial challenge for legged robots that is addressed "effortlessly" by biological networks abundant in nature, named central pattern generators (CPG). The multitude of CPG network models that have so far become biomimetic robotic controllers is not applicable to the emerging neuromorphic hardware, depriving mobile robots of a robust walking mechanism that would result in inherently energy-efficient systems. Here, we propose a brain-morphic CPG controler based on a comprehensive spiking neural-astrocytic network that generates two gait patterns for a hexapod robot. Building on the recently identified astrocytic mechanisms for neuromodulation, our proposed CPG architecture is seamlessly integrated into Intel's Loihi neuromorphic chip by leveraging a real-time interaction framework between the chip and the robotic operating system (ROS) environment, that we also propose. Here, we demonstrate that a Loihi-run CPG can be used to control a walking robot with robustness to sensory noise and varying speed profiles. Our results pave the way for scaling this and other approaches towards Loihi-controlled locomotion in autonomous mobile robots.

Related papers

• 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)
• Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion [3.7814142008074954]
  This paper introduces an astrocyte regulated Spiking Neural Network (SNN)-based CPG for learning locomotion gait through Reward-Modulated STDP for quadruped robots. The SNN-based CPG is simulated on a multi-object physics simulation platform resulting in the emergence of a trotting gait while running the robot on flat ground. $23.3times$ computational power savings is observed in comparison to a state-of-the-art reinforcement learning based robot control algorithm.
  arXiv  Detail & Related papers  (2023-12-25T20:33:16Z)
• DeepCPG Policies for Robot Locomotion [1.0057838324294686]
  novel DeepCPG policies that embed CPGs as a layer in a larger neural network. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies. Results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform.
  arXiv  Detail & Related papers  (2023-02-25T23:16:57Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• 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)
• A toolbox for neuromorphic sensing in robotics [4.157415305926584]
  We introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI.
  arXiv  Detail & Related papers  (2021-03-03T23:22:05Z)
• 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)
• A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards [1.8139771201780368]
  We propose a spiking neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. This category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.
  arXiv  Detail & Related papers  (2021-01-18T11:04:16Z)
• 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)

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.