Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics

When robots are needed to operate in a dynamic or unpredictable surroundings, this method has been proven success in the numerous robotic uses including navigation, manipulation & the decision-making. Robots can freely negotiate environment, overcome obstacles & the create ideal courses for navigation tasks using reinforcement learning methods. Reinforcement learning improves robots' accuracy & the dexterity in tasks of involving product handling including picking & the placing. Reinforcement learning is a crucial for the decision-making as it helps robots to evaluate possible actions, examine occurrences & adapt with the surroundings. Notwithstanding the great potential of reinforcements learning in robotics, numerous issues still exists including the necessity of significant data for training, the complexity of actual time decision-making & the guarantee of safe learning in the unstructured environments. Notwithstanding these challenges, ongoing research & the development helps to include reinforcement learning into robots, proving promising results in many different fields, from manufacturing to healthcare. By enabling robots to do tasks autonomously, adapt to unforeseen circumstances, and interact with humans in more intuitive and efficient ways, therefore increasing production and enhancing quality of life and setting the future of reinforcement learning in robotics to alter industries.

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in the context of robotics, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Three recent examples for the application of reinforcement learning to real-world robots are described: a pancake flipping task, a bipedal walking energy minimization task and an archery-based aiming task. In all examples, a state-of-the-art expectation-maximization-based reinforcement learning is used, and different policy representations are proposed and evaluated for each task. The proposed policy representations offer viable solutions to six rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, globality, multi-dimensionality and convergence. Both the successes and the practical difficulties encountered in these examples are discussed. Based on insights from these particular cases, conclusions are drawn about the state-of-the-art and the future perspective directions for reinforcement learning in robotics.

Technological advancements in robotics and cognitive science are contributing to the development of the field of cognitive robotics. Modern robotic platforms are able to exhibit the ability to learn and reason about complex tasks and to follow behavioural goals in complex environments. Nevertheless, many challenges still exist. One of these great challenges is to equip these robots with cognitive systems that allow them to deal with less constrained situations, beyond constrained scenarios as in industrial robotics. In this work we explore the application of the Reinforcement Learning (RL) paradigm to study the autonomous development of robot controllers without a priori supervised learning. Such a model-based RL architecture is discussed for the cognitive implications of applying RL in humanoid robots. To this end we show a developmental framework for RL in robotics and its implementation and testing for the iCub robotic platform in two novel experimental scenarios. In particular we focus on iCub simulation experiments with comparisons between internal perception-based reward signals and external ones, in order to compare learning performance of the robot guided by its own perception of action's outcomes with the one when the robot has its actions externally evaluated.

Reinforcement learning has become a promising and effective method to solve robots' complex control problems, enabling agents to learn optimal behaviors independently in a dynamic, uncertain, and usually high-dimensional environment. This paper comprehensively reviews the latest progress in applying reinforcement learning technology in robotics, paying special attention to key areas such as path planning, dynamic obstacle avoidance, multi-robot cooperation, and human-computer interaction. By critically analyzing various methods based on reinforcement learning, this paper emphasizes the key progress in improving robot autonomy, decision-making, and adaptability. It also discusses the challenges of deploying reinforcement learning in real-world robot applications, including sample efficiency, security, scalability, and generalization to new environments. In particular, Deep Reinforcement Learning (DRL) shows great potential in enhancing the robot's ability, especially in a complex, unstructured environment. However, challenges such as high computing costs and long training time still need to be addressed. Finally, the paper suggests that future research should focus on the hybrid RL method, improve learning efficiency, combine domain knowledge, and combine RL with other advanced technologies such as computer vision, multi-agent system, and real-time feedback mechanism, to further expand its potential application and increase its influence in the field of robotics.

Reinforcement learning holds the promise of enabling autonomous robots to learn behavioural skills with minimal human intervention. Reinforcement learning in robotics allows designing an algorithm that makes a robot learn a specific task by itself and arrive at an optimal policy that accomplishes its task without being specifically programmed to do so. This paper demonstrates the approach of using Deep reinforcement Learning in Teaching a quadruped robot how to walk, without programming it specifically to do so, and how to utilize the ability of self-learning robots in fault adaptive control algorithm based on reinforcement learning to teach the robot to adapt to faults and accomplish its mission.

This paper proposes an innovative deep learning algorithm, denominated as Neuroevolution of Hybrid Neural Networks in a Robotic Agent (NRNH-AR). This algorithm stands out in computational efficiency, evolutionary stability, and classification speed, surpassing other existing methods such as FAM-HGNN, I3A, DQN, among others. The triptych design of the NRNH-AR optimizes resource usage and exhibits superior adaptability in dynamic environments, which is a critical factor in robotics. The integration of artificial vision techniques enhances the capability for rapid classification of observations. Throughout 300 epochs in a dynamic environment, the importance of a minimal database for the algorithm’s effective evolution was evidenced. Moreover, it was found that the accuracy of the Convolutional Neural Network (CNN) directly impacts the performance of the NRNH-AR. An additional study of 850 epochs in a static environment provided a deeper understanding of the evolution and adaptation of learning networks. The NRNH-AR is positioned as a viable and promising solution in the field of applied deep learning in robotics.

Reinforcement Learning in Robotics: A Survey

Robot Operating System (ROS) has been widely used to develop robotic applications. The first generation of ROS generally lacks security features, and ROS 2 is introduced with security support. However, security concerns still exist for running ROS in practical multi-tenant environments. In this paper, we conduct an in-depth investigation into the security of ROS 2. We focus on vulnerabilities in ROS nodes and topics and intend to explore methods to break the isolation and security mechanisms systematically. We devise a set of strategies that can be exploited by attackers to escalate privilege or cause information leakage in a multi-tenant environment. These attacks can bypass existing isolation and security mechanisms, including ROS 2's native security module. To validate our findings, we employ simulations across various real-world scenarios to demonstrate how attackers could exploit these vulnerabilities to bypass existing security mechanisms. Finally, we present several defense practices to mitigate these identified threats.

Robot Operating system (ROS) is collection of software frameworks for robot software development. Researchers that work with ROS will have an opportunity to use or to be exact to reuse any software framework from another researcher as long as they use a similar robot platform, or similar device that has been include in the ROS. This paper present a design for a low cost rotating Lidar that compatible with ROS. As all we know distance measurement is the basic for building an autonomous capability for robots. Rotating lidar is among one the most popular device used to acquire this kind of information. However most of them are not suitable for low budget project because of their high cost. The design presented here tries to build a low cost rotating lidar based on Lidar Lite device with the compatibility with the ROS. The design incorporate a control board based on a microcontroller.

This paper describes the mechanical design and a cognition system for a novel concept-of-camera robotic assistant. The system combines the advantages of intra-abdominal devices and autonomous camera navigation. The robotic assistant is composed of a magnetic intra-abdominal camera robot with two internal cable-driven degrees of freedom and an external robot that handles an external magnet. The intelligence of the robot is implemented in a cognitive architecture based on a long-term memory that stores the robot's knowledge and learning capabilities to improve the robot's behavior. The navigation strategy combines a reactive control based on instrument tracking and a proactive control based on predefined behaviors, depending on the actual state of the task. The robot's learning capabilities include a semantic customization, to adapt the camera's behavior to the surgeon's preferences, and reinforcement learning, to improve the camera navigation strategy. This paper details both the hardware implementation of the system and the software implementation in a robotic operating system architecture. The cognition system and the performance of the cable-driven mechanism have been validated with a set of in vitro experiments. Moreover, the camera robot has been evaluated through an in-vivo experiment in a pig.

Currently, the Robot Operating System (ROS), is extensively used in various fields such as education, research, commercial and industry. This is due to the fact that ROS offers a range of packages that handle communication and hardware operation. However, it is widely known that ROS's security is still not robust. Transitioning from ROS to ROS2, which has enhanced security, might be somewhat challenging because some tasks might be in actual use in the industry or ongoing research projects. The issue of data security is also of paramount importance. There are currently various methods to hack into systems or intercept data in computer networks. After studying numerous related research works, we propose an encryption method for transmitted and received data using the AES algorithm, known as Layer-2. This method has been experimented with streaming images of four resolutions: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{320}\times \mathbf{240}, \mathbf{640}\times \mathbf{480},\mathbf{1280}\times \mathbf{720}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1920}\times \mathbf{1080}$</tex>. By comparing the frame rate and CPU usage during both unencrypted and encrypted streaming, it was found that the software developed according to the concept of this research can stream in an encrypted format at the same frame rate as unencrypted streaming, with an average maximum increase in CPU usage of 19.98% on the publishing side. The encryption concept in this research is designed based on a text-based protocol combined with the function overloading feature of object-oriented programming. This allows for a quick add-on to operating ROS software with minimal modifications. Furthermore, it can be adapted for use with other data transmission methods, such as Publish/Subscribe models like NATS, MQTT, etc.

Security for the Robot Operating System

One of the challenges of outdoor robots is developing effective portable Human-Robot-Interaction (HRI) frameworks. Hand-held devices offer a practical solution. By equipping these devices with robot software, they can be made to interact with the outdoor robots. Android devices are ideal as they are open source and can be integrated with robots powered by the Robot Operating System (ROS), also open source. However, due to the limits of rosjava, the mechanism that links ROS with android, and the conflicting modes of operation between ROS and android, current implementations of ROS-android offer limited robot applications that do not support advanced operations such as autonomous navigation and others. This paper implements selective compartmentalization to overcome these limitations, by combining ROS with android through a number of ROS and android bridges that would facilitate the development of advanced robot applications. Through the proposed method, authors were able to develop a portable HRI framework that allowed human operators to supervise an outdoor mobile robot while it performed an autonomous task. From their mobile devices, users were able to initialize the robot, configure its motion, and monitor its progress. Also, users were able to reprogram the robot to perform new tasks (not previously planned) through a creative use of features offered in the developed HRI framework. Also, user cognitive effort was reported to be low as evident by the positive score on the NASA-TLX scale test which was corroborated with robot performance data. This paper presents the detailed development and implementation steps.

Building customized control system for specific robot is generally acknowledged as the fundamental section of developing auto robots. To simplify the programming process and increase the reuse of codes, this research develops a general method of developing customized robot simulation and control system software with robot operating system (ROS). First, a 3D visualization model is created in URDF (unified robot description format), and is viewed in Rviz to achieve motion planning with MoveIt! software package. Second, the machine vision provided by camera driver package in ROS enables the use of tools for image process, 3D point cloud analysis to reconstruct the environment to achieve accurate target location. Third, the communication protocols provided by ROS like serial, Modbus support the communication system development. To examine the method, we designed a tomato harvesting dual-arm robot, and conducted farming experiment with it. This work demonstrates the advantages of ROS when applied in robot control system development, and offers a plain method of building such system with ROS.

Autonomous service robots are gaining popularity as they find application in different sectors such as logistics, hospitality, medical, space exploration and military. It is widely seen that autonomous service robots help increase efficiency, reduce cost and ensure safer operations. Automating material handling operations in highly dynamic indoor environments is risky and involves use of complex algorithms. However, continuously advancing technologies and machine programming tend to overcome these challenges. The three essential functions of an autonomous robot are to identify its instantaneous location in three-dimensional space, generate the environment map and decide an optimal path for locomotion. Simultaneous Localization and Mapping (SLAM) is a widely used algorithm for navigation in unfamiliar environments. This chapter presents a cost-effective autonomous ground robot for automating material handling, transportation and other repetitive tasks in dynamically changing surroundings. The robot is programmed to map the indoor environment and localize itself in the workspace with the help of SLAM and Adaptive Monte Carlo Localization (AMCL). In addition, the robot can manoeuvre through the indoor workspace by avoiding static and dynamic obstacles. This work employs the GMapping open-source package in the Robot Operating System (ROS) for performing SLAM with the aid of a laser range finder, wheel odometry and inertial data. The GMapping algorithm uses a probabilistic approach to construct a map and estimate the robot’s location in two-dimensional space. In addition, this chapter presents an autonomous navigation feature with the help of the AMCL method for localizing the robot within an uncertain environment and making the robot move to its goal using move base actions in ROS. The path planning of the mobile robot is classified into global and local plans. The global plan is responsible for planning the path from the start point to the endpoint. The local plan is responsible for planning the actual movement of the robot by avoiding any dynamic obstacles not mapped earlier. This work uses the Dijistra algorithm for the global and the dynamic window approach (DWA) algorithm for the local path planning. The optimization algorithm minimizes the error between the global and local paths. The simulation study of the proposed algorithm is performed in a virtual environment called Gazebo in ROS, which is nearly similar to a real-time indoor workspace. Further, a robot prototype is fabricated, and its performance is evaluated in an unknown environment. The robot prototype is equipped with brushed DC motors with optical encoder feedback for wheel odometry (ODOM), Light Detection and Ranging (LiDAR), and Inertial Measurement Unit (IMU) sensors controlled using a Jetson Nano. The simulation and experimental results showcase the robot’s successful two-dimensional localization and navigation in the factory environment. The current system has the potential for indoor assistance during material transfer and logistic applications. Further upgrades to the system can make it usable for Automated Storage and Retrieval System (ASRS) applications in warehouses/factories.