Toward evolutionary and developmental intelligence

Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics. Furthermore, a computational model that enables an artificial cognitive system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes the development of a cognitive architecture using probabilistic generative models (PGMs) to fully mirror the human cognitive system. The integrative model is called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In this paper, the process of building the WB-PGM and learning from the human brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network

NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigatio

Gaze control modelling and robotic implementation

Although we have the impression that we can process the entire visual field in a single fixation, in reality we would be unable to fully process the information outside of foveal vision if we were unable to move our eyes. Because of acuity limitations in the retina, eye movements are necessary for processing the details of the array. Our ability to discriminate fine detail drops off markedly outside of the fovea in the parafovea (extending out to about 5 degrees on either side of fixation) and in the periphery (everything beyond the parafovea). While we are reading or searching a visual array for a target or simply looking at a new scene, our eyes move every 200-350 ms. These eye movements serve to move the fovea (the high resolution part of the retina encompassing 2 degrees at the centre of the visual field) to an area of interest in order to process it in greater detail. During the actual eye movement (or saccade), vision is suppressed and new information is acquired only during the fixation (the period of time when the eyes remain relatively still). While it is true that we can move our attention independently of where the eyes are fixated, it does not seem to be the case in everyday viewing. The separation between attention and fixation is often attained in very simple tasks; however, in tasks like reading, visual search, and scene perception, covert attention and overt attention (the exact eye location) are tightly linked. Because eye movements are essentially motor movements, it takes time to plan and execute a saccade. In addition, the end-point is pre-selected before the beginning of the movement. There is considerable evidence that the nature of the task influences eye movements. Depending on the task, there is considerable variability both in terms of fixation durations and saccade lengths. It is possible to outline five separate movement systems that put the fovea on a target and keep it there. Each of these movement systems shares the same effector pathway—the three bilateral groups of oculomotor neurons in the brain stem. These five systems include three that keep the fovea on a visual target in the environment and two that stabilize the eye during head movement. Saccadic eye movements shift the fovea rapidly to a visual target in the periphery. Smooth pursuit movements keep the image of a moving target on the fovea. Vergence movements move the eyes in opposite directions so that the image is positioned on both foveae. Vestibulo-ocular movements hold images still on the retina during brief head movements and are driven by signals from the vestibular system. Optokinetic movements hold images during sustained head rotation and are driven by visual stimuli. All eye movements but vergence movements are conjugate: each eye moves the same amount in the same direction. Vergence movements are disconjugate: The eyes move in different directions and sometimes by different amounts. Finally, there are times that the eye must stay still in the orbit so that it can examine a stationary object. Thus, a sixth system, the fixation system, holds the eye still during intent gaze. This requires active suppression of eye movement. Vision is most accurate when the eyes are still. When we look at an object of interest a neural system of fixation actively prevents the eyes from moving. The fixation system is not as active when we are doing something that does not require vision, for example, mental arithmetic. Our eyes explore the world in a series of active fixations connected by saccades. The purpose of the saccade is to move the eyes as quickly as possible. Saccades are highly stereotyped; they have a standard waveform with a single smooth increase and decrease of eye velocity. Saccades are extremely fast, occurring within a fraction of a second, at speeds up to 900°/s. Only the distance of the target from the fovea determines the velocity of a saccadic eye movement. We can change the amplitude and direction of our saccades voluntarily but we cannot change their velocities. Ordinarily there is no time for visual feedback to modify the course of the saccade; corrections to the direction of movement are made in successive saccades. Only fatigue, drugs, or pathological states can slow saccades. Accurate saccades can be made not only to visual targets but also to sounds, tactile stimuli, memories of locations in space, and even verbal commands (“look left”). The smooth pursuit system keeps the image of a moving target on the fovea by calculating how fast the target is moving and moving the eyes accordingly. The system requires a moving stimulus in order to calculate the proper eye velocity. Thus, a verbal command or an imagined stimulus cannot produce smooth pursuit. Smooth pursuit movements have a maximum velocity of about 100°/s, much slower than saccades. The saccadic and smooth pursuit systems have very different central control systems. A coherent integration of these different eye movements, together with the other movements, essentially corresponds to a gating-like effect on the brain areas controlled. The gaze control can be seen in a system that decides which action should be enabled and which should be inhibited and in another that improves the action performance when it is executed. It follows that the underlying guiding principle of the gaze control is the kind of stimuli that are presented to the system, by linking therefore the task that is going to be executed. This thesis aims at validating the strong relation between actions and gaze. In the first part a gaze controller has been studied and implemented in a robotic platform in order to understand the specific features of prediction and learning showed by the biological system. The eye movements integration opens the problem of the best action that should be selected when a new stimuli is presented. The action selection problem is solved by the basal ganglia brain structures that react to the different salience values of the environment. In the second part of this work the gaze behaviour has been studied during a locomotion task. The final objective is to show how the different tasks, such as the locomotion task, imply the salience values that drives the gaze

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour

Can Control Hierarchies be Developed and Optimised Progressively?

Hierarchical structures are used in robots to achieve effective results in control problems. Hierarchical structures are found in a wide array of applications of AI and robotics, making them a key aspect of control. Even though they hold an integral part in control, such structures are typically produced heuristically, resulting in inconsistent performance. This means that effective control tasks or controllers perform poorly due to the hierarchy being badly defined, limiting what controllers can do. Complex control problems that require adaptive behaviour or autonomy remain challenging for control theorists, with complex problem domains making the heuristic process of producing complex hierarchies harder. It is evident that the heuristic process must have some form of procedure that could be turned into a methodology. By formalising or automating this process, control hierarchies can be produced with consistently effective results without relying on the heuristic production of a control engineer which can easily fail. This thesis proposes an algorithmic approach (inspired by Perceptual Control Theory) known as \ac{DOSA}. \ac{DOSA} produces heirarchies automatically using real world experience and the inputs the system has access to. This thesis shows that DOSA consistently reproduces effective hierarchies that exist in the literature, when billions of possible hierarchies were available. Furthermore, this thesis investigates the value of using hierarchies in general and their benefits in control problems. The computational complexity of hierarchies is compared, showing that while hierarchies do not have a computational advantage, the parameter optimisation procedure is aided greatly by hierarchical parameter optimisation. The thesis then proceeds to study th hierarchical optimisation of parameters and how hierarchies allow this process to be performed more consistently for better results, concluding that hierarchical parameter optimisation produces more consistent controllers that also transfer better to an unseen problem domain. Parameter optimisation is a challenge that also limits otherwise effective controllers and limits the use of larger structures in control. The research described in this thesis formalises the process of generating hierarchical controllers as well as hierarchically optimising them, providing a comprehensive methodology to automate the production of robust controllers for complex problems

Hierarchical generative modelling for autonomous robots

Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks