Trends in the control of hexapod robots: a survey

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.The first author received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (FCT) (Grant No. SFRH/BD/145818/2019), with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional Regional Norte. This work has been supported by the FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

Towards Sensorimotor Coupling of a Spiking Neural Network and Deep Reinforcement Learning for Robotics Application

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review on both spiking neural networks and deep reinforcement learning with emphasis on robotic applications. Then we will demonstrate how to develop a robotics application for context-aware scene understanding to perform sensorimotor coupling. Our system contains two modules corresponding to scene understanding and robotic navigation. The first module is implemented as a spiking neural network to carry out semantic segmentation to understand the scene in front of the robot. The second module provides a high-level navigation command to robot, which is considered as an agent and implemented by online reinforcement learning. The module was implemented with biologically plausible local learning rule that allows the agent to adopt quickly to the environment. To benchmark our system, we have tested the first module on Oxford-IIIT Pet dataset and the second module on the custom-made Gym environment. Our experimental results have proven that our system is able present the competitive results with deep neural network in segmentation task and adopts quickly to the environment

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Self-Generation of Reward by Moderate-Based Index for Senor Inputs

In conventional reinforcement learning, a reward function influences the learning results, and therefore, the reward function is very important. To design this function considering a task, knowledge of reinforcement learning is required. In addition to this, a reward function must be designed for each task. These requirements make the design of a reward function unfeasible. We focus on this problemand aim at realizing a method to generate a reward without the design of a special reward function. In this paper, we propose a universal evaluation for sensor inputs, which is independent of a task and is modeled on the basis of the indicator of pleasure and pain in biological organisms. This evaluation estimates the trend of sensor inputs based on the ease of input prediction. Instead of the design of a reward function, our approach assists a human being in learning how to interact with an agent and teaching it his/her demand. We recruited a research participant and attempted to solve the path planning problem. The results show that a participant can teach an agent his/her demand by interacting with the agent and the agent can generate an adaptive route by interacting with the participant and the environment

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book