Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task

Robot Navigation in Unseen Spaces using an Abstract Map

Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see https://btalb.github.io/abstract_map/ for access to softwar

Fuzzy associative memory for humanoid robot joint control

Traditional approaches to joint control required accurate modelling of the system dynamic of the plant in question. Fuzzy Associative Memory (FAM) control schemes allow adequate control without a model of the system to be controlled. This paper presents a FAM based joint controller implemented on a humanoid robot. An empirically tuned PI velocity control loop is augmented with this feed forward FAM, with considerable reduction in joint position error achieved online and with minimal additional computational overhead

Place Categorization and Semantic Mapping on a Mobile Robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module

Online learning of autonomous helicopter control

This paper details the development of an online adaptive control system, designed to learn from the actions of an instructing pilot. Three learning architectures, single layer neural networks (SLNN), multi-layer neural networks (MLNN), and fuzzy associative memories (FAM) are considerd. Each method has been tested in simulation. While the SLNN and MLNN provided adequate control under some simulation conditions, the addition of pilot noise and pilot variation during simulation training caused these methods to fail

Solving Navigational Uncertainty Using Grid Cells on Robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments

Using Strategic Movement to Calibrate a Neural Compass: A Spiking Network for Tracking Head Direction in Rats and Robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex

Adalimumab, etanercept and ustekinumab for treating plaque psoriasis in children and young people: systematic review and economic evaluation

Background: Psoriasis is a chronic inflammatory disease that predominantly affects the skin. Adalimumab (HUMIRA®, AbbVie, Maidenhead, UK), etanercept (Enbrel®, Pfizer, New York, NY, USA) and ustekinumab (STELARA®, Janssen Biotech, Inc., Titusville, NJ, USA) are the three biological treatments currently licensed for psoriasis in children. Objective: To determine the clinical effectiveness and cost-effectiveness of adalimumab, etanercept and ustekinumab within their respective licensed indications for the treatment of plaque psoriasis in children and young people. Data sources: Searches of the literature and regulatory sources, contact with European psoriasis registries, company submissions and clinical study reports from manufacturers, and previous National Institute for Health and Care Excellence (NICE) technology appraisal documentation. Review methods: Included studies were summarised and subjected to detailed critical appraisal. A network meta-analysis incorporating adult data was developed to connect the effectiveness data in children and young people and populate a de novo decision-analytic model. The model estimated the cost-effectiveness of adalimumab, etanercept and ustekinumab compared with each other and with either methotrexate or best supportive care (BSC), depending on the position of the intervention in the management pathway. Results: Of the 2386 non-duplicate records identified, nine studies (one randomised controlled trial for each drug plus six observational studies) were included in the review of clinical effectiveness and safety. Etanercept and ustekinumab resulted in significantly greater improvements in psoriasis symptoms than placebo at 12 weeks’ follow-up. The magnitude and persistence of the effects beyond 12 weeks is less certain. Adalimumab resulted in significantly greater improvements in psoriasis symptoms than methotrexate for some but not all measures at 16 weeks. Quality-of-life benefits were inconsistent across different measures. There was limited evidence of excess short-term adverse events; however, the possibility of rare events cannot be excluded. The majority of the incremental cost-effectiveness ratios for the use of biologics in children and young people exceeded NICE’s usual threshold for cost-effectiveness and were reduced significantly only when combined assumptions that align with those made in the management of psoriasis in adults were adopted. Limitations: The clinical evidence base for short- and long-term outcomes was limited in terms of total participant numbers, length of follow-up and the absence of young children. Conclusions: The paucity of clinical and economic evidence to inform the cost-effectiveness of biological treatments in children and young people imposed a number of strong assumptions and uncertainties. Health-related quality-of-life (HRQoL) gains associated with treatment and the number of hospitalisations in children and young people are areas of considerable uncertainty. The findings suggest that biological treatments may not be cost-effective for the management of psoriasis in children and young people at a willingness-to-pay threshold of £30,000 per quality-adjusted life-year, unless a number of strong assumptions about HRQoL and the costs of BSC are combined. Registry data on biological treatments would help determine safety, patterns of treatment switching, impact on comorbidities and long-term withdrawal rates. Further research is also needed into the resource use and costs associated with BSC. Adequately powered randomised controlled trials (including comparisons against placebo) could substantially reduce the uncertainty surrounding the effectiveness of biological treatments in biologic-experienced populations of children and young people, particularly in younger children. Such trials should establish the impact of biological therapies on HRQoL in this population, ideally by collecting direct estimates of EuroQol-5 Dimensions for Youth (EQ-5D-Y) utilities. Study registration: This study is registered as PROSPERO CRD42016039494. Funding: The National Institute for Health Research Health Technology Assessment programme

Adding a receding horizon to locally weighted regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum