Edge devices (ED) play a crucial role in enabling intelligent decision-making capabilities for Smart Cyber-Physical Systems (CPS). Nevertheless, it is a challenging objective, given the resource constraint of edge devices. Pre-trained Machine learning models fail to perform well against real-world data, provided via EDs. Conventional Embodied AI strategies outsource the training load to adapt to real-world data. However, the dependence on outsourcing attracts issues related to latency, privacy, scalability, and so on that are detrimental to a CPS. As a solution, this c proposes ChaoticImmuneNet , a lightweight Embodied AI method that allows onboard learning on resource-constrained EDs, with limited and noisy data. The article introduces a novel strategy, merging techniques from Artificial Immune Systems and Chaos Theory with a Siamese Neural Network for realizing onboard learning. ChaoticImmuneNet differs from Chaotic Neural Networks as the former does not require specialized models and hardware to leverage chaotic dynamics. Experimental studies carried out using three diverse image datasets demonstrate the efficacy of the proposed method when compared with existing onboard learning techniques, in terms of accuracy, time and storage requirements. A real-world deployment of the ChaoticImmuneNet on a real mobile robot, operating within a warehouse prototype testify to its pragmatic utility.

Evolutionary Algorithms (EA) have been leveraged to tackle the challenges faced while using GANs such as mode collapse, vanishing gradient, latent space search, etc. However, the existing techniques of using EA with GANs operate backpropagation and EA in isolation from each other, leaving ample room for further exploration. This paper creates a collaborative bridge between EA and GANs by exploring a neuroevolution method for utilising both EA and backpropagation-based optimisation, simultaneously, for a multi-generator GAN architecture. Experiments conducted using a standard dataset with variants of the proposed method highlight the towering impact of each of the components involved in the proposed method.