On the false positives and false negatives of the Jacobian Matrix in kinematically redundant parallel mechanisms

The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to non-redundant systems. In this paper, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies. The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables when forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have thus far remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures

Evolving recurrent neural network controllers by incremental fitness shaping

Time varying artificial neural networks are commonly used for dynamic problems such as games controllers and robotics as they give the controller a memory of what occurred in previous states which is important as actions in previous states can influence the final success of the agent. Because of this temporal dependence, methods such as back-propagation can be difficult to use to optimise network parameters and so genetic algorithms (GAs) are often used instead. While recurrent neural networks (RNNs) are a common network used with GAs, long short term memory (LSTM) networks have had less attention. Since, LSTM networks have a wide range of temporal dynamics, in this paper, we evolve an LSTM network as a controller for a lunar lander task with two evolutionary algorithms: a steady state GA (SSGA) and an evolutionary strategy (ES). Due to the presence of a large local optima in the fitness space, we implemented an incremental fitness scheme to both evolutionary algorithms. We also compare the behaviour and evolutionary progress of the LSTM with the behaviour of an RNN evolved via NEAT and ES with the same fitness function. LSTMs proved themselves to be evolvable on such tasks, though the SSGA solution was outperformed by the RNN. However, despite using an incremental scheme, the ES developed solutions far better than both showing that ES can be used both for incremental fitness and for LSTMs and RNNs on dynamic tasks

Modeling co-operative volume signaling in a plexus of nitric oxide synthase-expressing neurons

In vertebrate and invertebrate brains, nitric oxide (NO) synthase (NOS) is frequently expressed in extensive meshworks (plexuses) of exceedingly fine fibers. In this paper, we investigate the functional implications of this morphology by modeling NO diffusion in fiber systems of varying fineness and dispersal. Because size severely limits the signaling ability of an NO-producing fiber, the predominance of fine fibers seems paradoxical. Our modeling reveals, however, that cooperation between many fibers of low individual efficacy can generate an extensive and strong volume signal. Importantly, the signal produced by such a system of cooperating dispersed fibers is significantly more homogeneous in both space and time than that produced by fewer larger sources. Signals generated by plexuses of fine fibers are also better centered on the active region and less dependent on their particular branching morphology. We conclude that an ultrafine plexus is configured to target a volume of the brain with a homogeneous volume signal. Moreover, by translating only persistent regional activity into an effective NO volume signal, dispersed sources integrate neural activity over both space and time. In the mammalian cerebral cortex, for example, the NOS plexus would preferentially translate persistent regional increases in neural activity into a signal that targets blood vessels residing in the same region of the cortex, resulting in an increased regional blood flow. We propose that the fineness-dependent properties of volume signals may in part account for the presence of similar NOS plexus morphologies in distantly related animals

An environmental model of self-compatibility transitions in the solanaceae plant family

Higher level selection processes such as species selection are not generally predicted to overpower individual selection on character traits. Goldberg et al. provide a model derived from collected life history data and argue that species selection is maintaining self-incompatibility in the Solanaceae plant family. This model applies only on the level of the species, not representing the underlying interactions between individuals and the environment. We propose a new model with environmental variation at the individual level that may explain the maintenance and frequency of loss of this character trait. We use individual based modelling techniques to explore our hypothesis, and compare it with that originally proposed. The results show alternative values required for the mutation rate to produce the species level transition frequency under the opposing models, given certain assumptions. Future work is suggested to refine the parameter relationships, test for robustness, and determine if individual models of higher complexity will exhibit similar outcomes

Simulating soft-bodied swimmers with particle-based physics

In swimming virtual creatures, there is often a disparity between the level of detail in simulating a swimmer’s body and that of the fluid it moves in. In order to address this disparity, we have developed a new approach to modelling swimming virtual creatures using pseudo-soft bodies and particle-based fluids, which has sufficient realism to investigate a larger range of body-environment interactions than are usually included. As this comes with increased computational costs, which may be severe, we have also developed a means of reducing the volume of fluid that must be simulated

Active shape discrimination with compliant bodies as reservoir computers

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive

A situated and embodied model of ant route navigation

This abstract summarises a model of route navigation inspired by the behaviour of ants presented fully in Baddeley et al. (2012). The ant's embodiment coupled with an innate scanning behaviour means that robust route navigation can be achieved by a parsimonious biologically plausible algorithm

Insect-inspired navigation: Smart tricks from small brains

Small-brained insects are expert at many tasks that are currently difficult for robots, but especially in the speed and robustness of their learning abilities. In contrast to AI methods which generally take long times to train and large amounts of labelled data, insects are rapid learners of visual and olfactory information and are capable of long distance navigation, exploration and spatial learning. What if we could give robots these abilities, by mimicking the sensors, circuits and behaviours of insects? This is the goal of the Brains on Board project (brainsonboard.co.uk). In this talk, we will discuss the Brains on Board project and our work on insect-inspired visual navigation in particular. The use of visual information for navigation is a universal strategy for sighted animals, amongst whom ants are particular experts despite have small brains and low-resolution vision [1]. To understand how they achieve this, we combine behavioural experiments with modelling and robotics to show how ants directly acquire and use task-specific information through specialised sensors, brains and behaviours, enabling complex behaviour to emerge without complex processing. In this spirit, we will show that an agent – insect or robot – can robustly navigate without ever knowing where it is, without specifying when or what it should learn, nor requiring it to recognise specific objects, places routes or maps. This leads to an algorithm in which visual information specifies actions not locations in which route navigation is recast as a search for familiar views allowing routes through visually complex worlds to be encoded by a single layer artificial neural network (ANN) after a single training run with only low resolution vision [2]. As well as meaning that the algorithms are plausible in terms of memory load and computation for a small-brained insect, it also makes them very well-suited to a small, power-efficient, robot. We thus demonstrate that this algorithm, with all computation performed on a small low-power robot, is capable of delivering reliable direction information along outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain) [3]. Indeed, routes can be precisely recapitulated and we show that the required computation does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than the compact representation losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing. [1] Shettleworth, S. (2010) Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences 14 (11):477-481 [2] Baddeley, B., Graham, P., Husbands, P., & Philippides, A. (2012). A model of ant route navigation driven by scene familiarity. PLoS computational biology, 8(1), e1002336. [3] Knight, J, Sakhapov, D., Domcsek, A., Dewar, A., Graham, P., Nowotny, T., Philippides, A. (2019) Insect-Inspired Visual Navigation On-Board an Autonomous Robot: Real-World Routes Encoded in a Single Layer Network. Proc. Artificial Life 19. In Press

Neural coding in the visual system of Drosophila melanogaster: how do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called ‘ring neurons’, projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons’ receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour

Spike-timing dependent plasticity and the cognitive map

Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent “theta-coded” temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development