To achieve an efficient compression and reconstruction of vibrotactile signals in the Tactile Internet, an end-to-end neural vibrotactile codec, PC-NSVC, is proposed. By integrating a residual product quantizer (RPQ) within a deep autoencoder, PC-NSVC effectively reduces coding latency through joint training and inference of the entire framework, while simultaneously enhancing the quality of the reconstructed signals. The RPQ allows for control over transmission bitrates by adjusting quantizer parameters, enabling scalable codec across various network environments and bandwidths. Additionally, PC-NSVC incorporates psychohaptic model to account for the influence of human perception, further improving the perceptual fidelity of the reconstructed signals. A remote vibrotactile sharing prototype, TouchShare, was developed to conduct transmission and material classification tests. Simulation and transmission results demonstrate that the PC-NSVC scheme significantly improves the quality of reconstructed signals at different compression ratios and supports accurate material classification, outperforming existing schemes.

In this paper, a novel joint source-channel coding (JSCC) scheme based on the residual vector quantized diffusion model is proposed for task-oriented agent communication. To make the agent communication compatible with existing digital communication systems, the feature vectors encoded by the parameterized encoder are quantized using multiple learnable codebooks, forming the quantized features. Furthermore, to improve the reliability of quantized feature transmission, a discrete channel denoising diffusion model (DCDDM) is designed. The DCDDM can learn the distribution of the sender agent’s quantized features, and then utilize this learned knowledge to correct the receiver agent’s received quantized features. Based on the corrected feature vectors, the parameterized decoder recovers task-related information for the receiver agent’s subsequent decision-making. Finally, the model parameters of the proposed scheme are trained within the reinforcement learning framework. In addition, we design a task-oriented agent communication proof-of-concept prototype. Simulation and experiment results show that the proposed scheme has better generalization capability than the existing schemes. That is, with the proposed scheme, the transmission of task-related information is more reliable, thus the multi-agent systems can collaborate better under different channel conditions.

We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.

In this paper, we propose an age of incorrect information (AoII)-aware data dissemination scheme for distributed multi-agent systems (MASs). In the proposed scheme, AoII is utilized to measure the importance of data in terms of timeliness and content. We formulate the joint optimization of time slot allocation and agent selection as a decentralized partially observable Markov decision process (Dec-POMDP), with the objective of minimizing the AoII. To solve the Dec-POMDP, a novel multi-agent reinforcement learning algorithm (DV-MAPPO) is proposed. In particular, to tackle challenges posed by the partial observability of global system information, each agent estimates the global system state using variational inference. Moreover, to improve the accuracy of global system state estimation, each agent is given an intrinsic reward that is dominated by the accuracy of estimates. The proposed data dissemination scheme is implemented and evaluated in various missions. Simulation results show that the proposed data dissemination scheme outperforms traditional data dissemination schemes in terms of AoII. Furthermore, in typical multi-agent collaborative tasks, the proposed scheme facilitates more efficient cooperation among multiple agents compared to the data distribution mechanisms that ignore the importance of data.