Evaluation of a Task Allocation Method Using a Deep Reinforcement Learning for Multiple Service Robots

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 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.

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.

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.

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.

The paper does not provide any original contribution. Therefore, it cannot be considered a research paper. No results are included. If it is supposed to be a review article, then it is poorly written. The paper should be rejected.

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.

We present a novel statistical approach to incorporate uncertainty awareness in model-free distributional deep reinforcement learning for mission and safety-critical robotics. Deep learning predictions are influenced by uncertainties in the data, termed as aleatoric uncertainties, as well as uncertainties in the learning process and model structure, known as epistemic uncertainties. The proposed algorithm, called as Calibrated Evidential Quantile Regression in Deep-Q Networks (CEQR-DQN), addresses key challenges associated with separately estimating aleatoric and epistemic uncertainty in stochastic robotic environments. It combines deep evidential learning with quantile calibration based on the principles of conformal inference to provide explicit, sample-free computations of global uncertainty as opposed to local estimates based on simple variance. Thereby, the proposed approach overcomes limitations of traditional methods in computational and statistical efficiency and handling of out-of-distribution (OOD) observations. Tested on a suite of representative miniaturized Atari games (i.e., MinAtar), CEQR-DQN is shown to surpass similar existing frameworks in scores and learning speed. Its ability to rigorously evaluate uncertainties improves exploration strategies and can serve as a blueprint for other uncertainty-aware robotic algorithms.

Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. The Robotics Operating System (ROS) and Reinforcement Learning (RL) are two promising approaches that help accomplish precise control, seamless integration of sensors-actuators, and exhibit learned behavior. The ROS enables seamless communication between heterogeneous components, while RL focuses on learning optimal behaviors through trial-and-error scenarios. Combining the ROS and RL offers superior decision making, improved perception, enhanced automation, and reliability. This work focuses on investigating ROS-based RL applications across various domains, aiming to enhance understanding through comprehensive discussion, analysis, and summarization. We base our evaluation on the application area, type of RL algorithm used, and degree of ROS–RL integration. Additionally, we provide summary of seminal works that define the current state of the art, along with GitHub repositories and resources for research purposes. Based on the review of successfully implemented projects, we make recommendations highlighting the advantages and limitations of RL techniques for specific applications and environments. The ultimate goal of this work is to advance the robotics field by providing a comprehensive overview of the recent important works that incorporate both the ROS and RL, thereby improving the adaptability of these emerging techniques.

To facilitate the current and future automation needs, the research community constantly seeks to develop dynamic and efficient autonomous decision-making agents. These agents must not only be robust to modeling uncertainties, internal and external changes, but can adapt to a range of tasks also. Recent progress in deep reinforcement learning has corroborated to its potential to train such autonomous and robust agents.

Reinforcement Learning in Robotics: A Survey

In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on exploration learning, respectively. How to combine these two fields to better improve the development of the whole machine learning field is the dream of numerous researchers. The Deep Reinforcement Network (DRN) model successfully embedded reinforcement learning into the recommendation system, which provided a good idea for subsequent researchers. However, the disadvantage is also obvious, that is, the DRN model is built for news recommendations, meaning that the DRN model is not transferable, which is also the defect of many current recommendation system models. Meanwhile, the agent learning method adopted by the DRN model is primitive and inefficient. Among many models and algorithms that have emerged in recent years, we use the newly proposed deployment efficiency to measure their comprehensive quality and found that few models focus on both efficiency and performance improvement. To fill the gap of model deployment efficiency neglected by many researchers and to create a model of reinforcement learning agents with stronger performance, we have been exploring and trying to complete research on the Gate Attentional Factorization Machines (GAFM) model. Finally, we successfully integrated the GAFM model and reinforcement learning. The Deep Reinforcement Factorization Machines (DRFM) model proposed in this paper is based on the combination of deep learning with high perception ability and reinforcement learning with high exploration ability, centered on improving the deployment efficiency and learning performance of the model. The GAFM model is modified and upgraded using multidisciplinary techniques, and a new model-based random exploration strategy is proposed to update and optimize the recommendation list efficiently. Through parallel contrast experiments on various datasets, it is proved that the DRFM model surpasses the traditional recommendation system model in all aspects. The DRFM model is far superior to other models in terms of performance and robustness, and also significantly improved in terms of deployment efficiency. At the same time, we conduct a comparative analysis with the latest deep reinforcement learning algorithm and prove the unique advantages of the DRFM model.

Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.