On the Generalization Capability of Evolved Counter-propagation Neuro-controllers for Robot Navigation

Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross t…

Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp

Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational resources to perform the required control tasks. Neuromorphic computing stands for a promising research direction for addressing such problem. By mimicking the biological process for transferring information between neurons using spikes or impulses, spiking neural networks (SNNs) allow for low power consumption and asynchronous event-driven processing. In this paper, we propose an…

面向生物启发的视觉导航：一种用于微型无人机（MAV）的神经形态导航范式

Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs

本文提出一种用于MAV的端到端事件驱动神经形态导航范式，其性能与ANN方法相当，功耗显著降低，为低功耗自主导航提供了生物启发方案。

为实时适应节律刺激而演化的中枢模式发生器

Central pattern generators evolved for real-time adaptation to rhythmic stimuli

提出一种采用仿生CPG的虚拟四足机器人，通过多目标进化算法优化运动速度、方向及波动输入过滤层，使其能自发同步并调整步态以匹配外部节律刺激，从而在形态差异下实现协调运动及学习新模式。

Evolving neural networks to follow trajectories of arbitrary complexity

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by addi…