# AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy

Marios Sophocleous, Christina Lessi, Zhao Xu, Jakub Špaňhel, Renxi Qiu, Adrian Lendinez, Ioannis Chondroulis, Ioannis Belikaidis

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Marios Sophocleous, Christina Lessi, Zhao Xu, Jakub Špaňhel, Renxi Qiu, et al. AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.61-70, 10.1007/978-3-031-08341-9_6. hal-05335625

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# AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy

Marios Sophocleous<fnref n="1" />(✉), Christina Lessi<fnref n="2" />, Zhao Xu<fnref n="3" />, Jakub Špaňhel<fnref n="4" />(✉), Renxi Qiu<fnref n="5" />, Adrian Lendinez<fnref n="5" />, Ioannis Chondroulis<fnref n="6" />, and Ioannis Belikaidis<fnref n="6" />

**Abstract.** Innovative 5G orchestration architectures so far, have been mainly designed and optimized for Quality of Service (QoS), but are not aware of Quality of Experience (QoE). This makes intent recognition and End-to-End interpretability an inherited problem for orchestration systems, leading to possible creation of ineffective control policies. In this paper, an AI-driven intent-based networking for autonomous robots is proposed and demonstrated through the 5G-ERA project. In particular, to map an intent from individual vertical action to a global OSM control policy, a workflow of four tools is proposed: i) Action Sequence Generation, ii) Network Intent Estimation, iii) Resource Usage Forecasting, and iv) OSM Control Policy Generation. All of these tools are described in the paper with specific function descriptions, inputs, outputs and the semantic models/Machine Learning tools that have been used. Finally, the paper presents the developed intent-based dashboard for the visualization of the tools' outputs, whilst taking QoE into consideration.

**Keywords:** 5G · Intent-based networking · Enhanced robot autonomy · 5G-ERA · Machine learning · Semantic models · Autonomous robots

## 1 Introduction

5G technology has received a significant interest from the scientific and industrial community in the last decade. The advantages of the 5G technology are especially attractive for a variety of sectors, such as the robotics industry. Currently, innovative 5G orchestration architectures have been mainly designed for service delivery [1–3] without vertical-specific knowledge. Information models are the basis of the architectures on managing

<footnote n="1">¹ Arch. Makariou III and Mesaorias 1, eBOS Technologies Ltd, 2090 Lakatamia, Nicosia, Cyprus

marioss@ebos.com.cy</footnote>

<footnote n="2">² Hellenic Telecommunications Organization (OTE) S.A, 99 Kifissias Avenue, 15124 Maroussi, Athens, Greece</footnote>

<footnote n="3">³ NEC Laboratories Europe, Kurfürsten-Anlage 36, 69115 Heidelberg, Germany</footnote>

<footnote n="4">⁴ Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic</footnote>

<footnote n="5">⁵ School of Computer Science, University of Bedfordshire, Luton, UK</footnote>

<footnote n="6">⁶ WINGS ICT Solutions, 189 Siggrou Avenue, 17121 Athens, Greece</footnote>
the life cycle of the services and resources [3, 4]. Those models are largely based on
the concept of Anaemic Domain Models [5] optimized for creating, reading, updating,
and deleting (CRUD) services in a procedural style. As a result, implicit intents of the
services are not really taken into consideration in the approach hence, orchestrators are
optimized only for Quality of Service (QoS) without taking Quality of Experience (QoE)
into consideration. This makes intent recognition and End-to-End (E2E) interpretabil-
ity an inherited problem for orchestration systems, leading to the possible creation of
ineffective control policies [6]. This issue has been partially addressed in some existing
5G vertical applications by limiting the number of technology stacks. However, robotic
verticals normally require components from multiple vendors with multiple technology
stacks [7–10]. 5G experimental facilities need a solution that maintains intent recognition
and maps the user requirement into measurable network KPIs when multiple technology
stacks are involved.

In this paper, AI-driven intent-based networking, from the semantic models and
machine learning tools to the visualization dashboard, for 5G enhanced robot autonomy
is presented. The intent-based networking is to capture users’ (vertical customers) intent
and to align continuously the E2E networking to the recognized intents. It aims to
improve QoE for 5G-based autonomous robots. 5G robotic applications have received
considerable attention in recent years. While most of the research has been focused
on closed-loop control; the effectiveness of this approach alone is questionable in the
robotics community as it overlooks a key experience of autonomous robots - the need for
robot autonomy, particularly, in real-world robotic applications. By shifting knowledge
and learning from individual robots to the edges and the central cloud, 5G is able to
establish connected robotic intelligence, and subsequently realize and enhance robot
autonomy with the connected intelligence.

## 2 Concept of Intent-Based Networking for Autonomous Robots

The aim of the intent-based networking for autonomous robots is to optimize the QoE
of 5G orchestrators for vertical applications. For optimized experience on individual
5G-based autonomous robots, 5G needs to specify and optimally allocate resources
required by the connected intelligence. The intent-based networking predicts the need
for intelligence from the intents and specifies policy on individual applications to deliver
management, topology, placement, and resource optimization within 5G and cloud envi-
ronments. The autonomous use cases under the connected intelligence require computing
and storage to be shifted dynamically and repeatedly among robots, edges, and the central
cloud. Partial information will be replicated among Network Services (NSs) deployed
in different locations. To tailor NSs under the intent-based networking, different con-
figurations of Virtual Network Functions (VNFs) and slices are required [11–13]. The
creation of slices, at the level of complexity of the use cases, is still problematic for
exiting testbeds. 5G experimental facilities need to be adapted towards cloud-native
approach for efficient service delivery on the enhanced robot autonomy. Meanwhile, the
intent-based networking also enhances the existing NSs using cloud native approach,
with respect to the scalability, availability and feature velocity of the NSs as expected by autonomous robots. Therefore, the development of the intent-based networking fills the gaps between existing 5G product vision and cloud native deployment required by the robot autonomy enhancement. Overall, the development of intent-based networking for autonomous robots reveals how operational processes of essential robotic capabilities can be integrated into a rich domain model on supporting advanced orchestration.

In this paper vertical specific QoE models will be identified as the basis of con-
texts for service interoperability and service topology. The patterns and contexts will
be realized life cycle management. The contexts enable patterns to be tangible in their
specific sub-domains for further verification. The innovation leads to an automated and
interpretable mechanism for deriving the placement of network functions, order of com-
ponent instantiation. The innovation paves the way for application-driven approaches
toward automatic configuration on testbeds using Machine Learning (ML) and Artificial
Intelligence (AI). The concept also led to a cloud native service delivery under Microser-
vices architecture. Computing would be shifted among the robots, Multi-Access Edge
Computing (MECs) and the central cloud under connected intelligence. The models will
be optimized for MEC and central cloud for NSs' synchronization with data consistency.
The conceptual design illustrated in the following sections enables the composition a
distributed application from separately deployable services as required by Edgy DevOps
[6] The innovation enables NetApps and Orchestrators to access the robotic resources by
translating QoS of VNFs (instantiation, scaling, updating and termination) to efficient
robot control policy.

3 Semantic Models and Machine Learning Tools

To achieve the QoE oriented networking for 5G-based autonomous robots, a set of semantic models and Machine Learning tools were developed. By design, each tool can fulfill a function independently with inputs from other functions, and in the meanwhile, all the functions are linked together as a loop to continuously align the network management with the identified behaviors of 5G enhanced robots. In particular, to map an intent from individual vertical action to a global Open-Source MANO (OSM) control policy, we propose a workflow with the following four tools, illustrated in Fig. 1. With the designed workflow, the network management and orchestration can be extended to stay aligned with the actions of robots, which enhances their capability, and facilitates the autonomous robots to carry out different vertical-specific tasks.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** AI-driven intent-based management workflow with the semantic and ML tools.</figcaption></figure>

## 3.1 Action Sequence Generation

The semantic tool Action Sequence Generation maps the intent of a given task (e.g., go-to-kitchen) under the vertical application to a sequence of robot actions. This is learned from historical data, and integrates domain knowledge. The results are stored in a knowledge base. High level unstructured task requirement from users will be translated into specific and structured requirement of tangible actions. The actions can be retrieved from predefined containers or physical functions for instantiation, scaling and management. They lead to a scalable approach that translates vertical intent on capabilities into a network intent on resources. The vertical intents are defined in the semantic model based on “How do we store the intent within semantic model?”. A task table is prepared in the Semantic Knowledge Base (KB) to hold the intent selected by domain driven design. It records:

1. The robots, which are running for a particular task/intent.

2. The time at which they were assigned to the intent.

3. The status of the execution of the intent.
<table><caption>**Table 1.** Semantic interpretation of robot tasks</caption><thead><tr><th colspan="2">Semantic tool: action sequence generation</th></tr><tr><th>Function description</th><th>Predicting potential actions involved in a pre-defined task</th></tr></thead><tbody><tr><td>Inputs</td><td><em>From end users:</em><br/>Name of the task<br/>Outputs of ML Tool: OSM Control Policy Generation<br/>Approval or Rejection of the task<br/>Feedback from OSM policy, in case it is rejected<br/><em>From Semantic Knowledge Base:</em><br/>Historical information of actions (successful rate, quality of experience, and blocked experience)<br/>Historical information of actors (Robot, Cloud, or Edge) and NetApps (placement of the containers, availability of the slices, completion of the actions)</td></tr><tr><td>Outputs</td><td>Expected action sequence for completing the task. Corresponding actor for the actions</td></tr><tr><td>Methods</td><td>State Based Decision Process &amp; Experience Blocking Index</td></tr></tbody></table>

## 3.2 Network Intent Estimation

The function Network Intent Estimation is implemented with both semantic and ML tools. They aim to learn the mapping between the identified robot actions and the demanded network resources in a local site. Every provided robotic service has its own QoS requirements with respect to its purpose and implementation. The QoS demands may differ based on the placement location of this service even based on the requester. The semantic tool will look up the pre-defined static mappings and probabilistic distributions of the actions. The ML tool will learn new mappings from the existing ones. A basic description of the toolbox interface is provided in Table 2.

*Methods Description:* The semantic and machine learning tool performs individual resource planning through two independent mechanisms.

*Static Mapping:* The default variant based on deterministic table lookup according to pre-defined information in the KB with respect to the requested service and actor. The mean value for each metric part is used in case of multiple records or historical data available.

*Forecasted QoS:* This method exploits multiple records for each service with respect to the timeline order of these records (time-series), their seasonality, influence of holidays and forecast uncertainty.
<table><caption>**Table 2.** Description of the tool for mapping intent to resource usage</caption><thead><tr><th colspan="2">Semantic & machine learning tools: mapping intent to resource usage</th></tr></thead><tbody><tr><th>Function description</th><td>Translate expected action sequence into expected QoS and placement of the Kubernetes-based VNF (KNF)</td></tr><tr><th>Inputs</th><td>Action sequence from Tool 1<br>Actors from Tool 1<br>Table of QoS expectation per action stored in KB<br>Historical data about real measured requirements by Actors/Services (optional)</td></tr><tr><th>Outputs</th><td>Placement of the KNFs/VNFs and expected resources per network function for an individual robot</td></tr><tr><th>Methods</th><td>Deterministic table lookup based on pre-defined information in KB<br>Forecasted QoS based on pre-defined information in KB and historical data</td></tr></tbody></table>

## 3.3 Resource Usage Forecasting

The ML tool Resource Usage Forecasting analyses resource usage of a VNF/KNF associated with the robot. It will forecast the confidence of achieving the network intent in the near future. The basic function and technical details of the tool are summarized as the table below:

<table><caption>**Table 3.** Resource usage forecasting description</caption><thead><tr><th colspan="2">Machine learning tool: resource usage forecasting</th></tr></thead><tbody><tr><th>Function description</th><td>Analyze the historical resource usage, traffic load and other related telemetry, to forecast the future resource usage, and thus estimate the confidence of satisfying the intent, i.e., the allocated resource</td></tr><tr><th>Inputs</th><td>Intents: a set of numbers, e.g., allocated resources (CPU/memory usage) and required latency and throughput<br>Time series data about traffic load and other related telemetry, e.g., channel quality etc<br>Time series data about CPU usage etc</td></tr><tr><th>Outputs</th><td>Probabilistic distribution (confidence) that the intents can still be satisfied in the near future</td></tr><tr><th>Methods</th><td>Probabilistic models with uncertainty estimation</td></tr></tbody></table>

## 3.4 OSM Control Policy Generation

The ML tool OSM Control Policy Generation learns an OSM control policy. It makes decisions to accept/reject resource allocation plan or trigger a replanning request. Please refer to the following table for the technical details of the tool:
<table><caption>**Table 4.** OSM control policy generation description.</caption><tr><td>Machine learning tool:</td><td>OSM control policy generation</td></tr><tr><td>Function description</td><td>Analyze the historical data of the infrastructure to predict its behavior according to the predicted resource usage. Estimate the deployment feasibility to accept or reject the resource allocation plan</td></tr><tr><td>Inputs</td><td>Resource usage estimation<br>Resource availability prediction<br>Infrastructure-level resource usage/availability (historical data)</td></tr><tr><td>Outputs</td><td>OSM input: Accepted resource allocation plan<br>Tool 1 input: new resource allocation suggestion (rejected resource allocation plan - replanning)</td></tr><tr><td>Methods</td><td>AI-algorithms (Q-learning, clustering, etc.)</td></tr></table>

# 4 Intent-Based Visualization Tools

An Intent-based, front-end Dashboard (IBD) has been developed to enable the use of the Semantic & Machine Learning (ML) tools, described in the previous section, for the better resource management of the network. Although each robot vendor might have an existing User Interface (UI) in order to control and communicate with their robots, there is no UI for the 5G-ERA's unique ML tool for resource usage prediction based on the user's intent and providing better performance, Quality of Service (QoS) and Quality of Experience by the dynamic control of the network resources. Network orchestrators, such as Open-Source MANO (OSM) usually have their own dashboard however, that dashboard only shows the network's resources hence, the results of the ML tool can't be integrated onto that interface.

IBD development can be separated into two parts, the back-end and the front-end. The back-end, responsible to communicate with the database to obtain and format the data was developed using .NET CORE [14] 3.1 framework and C#. The main purpose of the back-end is to connect to the database through an Application Programming Interface (API) and obtain the required data. Once the data is obtained from the database, the format that the data must have to be pushed to the front-end is also edited by the back-end. Additionally, the back-end is responsible to connect to the SQL server that has all the user login information with assigned access levels and site access. The main idea is that specific users will have different access levels, i.e., one user can only be an observer and another user can have higher authorization levels with full access to the IBD. The front-end was developed using HTML, CSS JavaScript, and jQuery technologies. The front-end is responsible to obtain the formatted data from the back-end and present them in a specific format/way e.g., a graphical representation of the data.

## 4.1 Intent-Based Dashboard Architecture

The general architecture of the IBD is based on the standard Model-View-Controller (MVC) approach. The actual architecture of the IBD is shown in Fig. 2 below. In this
implementation, another component is introduced, the business logic that is responsible
to communicate with the database's API and obtain the required data.

<figure><img src="image_4.png" /><figcaption>Fig. 2. IBD architecture and connectivity to the database.</figcaption></figure>

## 4.2 Functionalities

Through the developed IBD users have their own login details to be used on the login page (Fig. 3) and each user will have his/her own access rights, specific use case (UC), and access to specific controls and these will be stored along with the user's login details. Each user can save their own customization formats and see data from the specific use case they are involved. Other than the authorization levels and direct link of an account with a specific UC, the developed IBD is capable of some visual customizations from the user such as the light or dark mode as well as the choice of changing the background colors. In terms of login security, the authentication security system from.NET Core framework (Microsoft) was used.

IBD is divided into four sections highlighting the four stages of the semantic & ML tools described in the previous section. Other than just the 4-sections, IBD has a navigation pane on the left. Although IBD was not intended to replace the proprietary HMIs of the UC executors and the robot users, based on the obtained UC requirements, it was proposed that such privileges would be very useful in the execution of the trials (Fig. 4). In terms of security, the same level of security implemented for the IBD login, will be valid for the HMI too.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Login page.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Portal from the IBD to a vendors’ web-based HMI.</figcaption></figure>

# 5 Conclusions

In this paper, the semantic models and the machine learning tools for 5G enhanced robot autonomy were presented. The tools, orchestrating the diverse resources to align with intents of the autonomous robots, are sequentially connected into a loop, including: the semantic tool Action Sequence Generation that maps the intent of a given task under the vertical application to a sequence of robot actions, the tool Net-work Intent Estimation which is responsible for the mapping, and the ML tool Resource Usage Forecasting that analyses resource usage of a VNF/KNF associated with the robot, as well as the tool OSM Control Policy Generation to estimate the deployment feasibility of a resource allocation plan. Additionally, the IBD development was presented and its separation into two parts, the back-end and the front-end, was described.
**Acknowledgments.** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Grant Agreement No 101016681.

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