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Background: Virtual reality combined with a spherical treadmill is used across species for studying neural circuits underlying navigation and learning.
New method: We developed an optical flow-based method for tracking treadmill ball motion in real time using a single high-resolution camera.
Results: Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms. The system was tested for behavior with fruit flies. The application and source code are available at https://github.com/ivan-vishniakou/neural-circuits-vr.
Comparison with existing method(s): Optical flow-based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration.
Conclusions: A fast system for rotational ball motion tracking suitable for virtual reality behavior with fruit flies was developed and characterized.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.
SISAL: User manual
(1990)
Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm
(2019)
This paper presents a modification of the data-driven sensor-based fault detection and diagnosis (SFDD) algorithm for online robot monitoring. Our version of the algorithm uses a collection of generative models, in particular restricted Boltzmann machines, each of which represents the distribution of sliding window correlations between a pair of correlated measurements. We use such models in a residual generation scheme, where high residuals generate conflict sets that are then used in a subsequent diagnosis step. As a proof of concept, the framework is evaluated on a mobile logistics robot for the problem of recognising disconnected wheels, such that the evaluation demonstrates the feasibility of the framework (on the faulty data set, the models obtained 88.6% precision and 75.6% recall rates), but also shows that the monitoring results are influenced by the choice of distribution model and the model parameters as a whole.
While executing actions, service robots may experience external faults because of insufficient knowledge about the actions' preconditions. The possibility of encountering such faults can be minimised if symbolic and geometric precondition models are combined into a representation that specifies how and where actions should be executed. This work investigates the problem of learning such action execution models and the manner in which those models can be generalised. In particular, we develop a template-based representation of execution models, which we call delta models, and describe how symbolic template representations and geometric success probability distributions can be combined for generalising the templates beyond the problem instances on which they are created. Our experimental analysis, which is performed with two physical robot platforms, shows that delta models can describe execution-specific knowledge reliably, thus serving as a viable model for avoiding the occurrence of external faults.
We describe the development of an architecture for the DESIRE technology demonstrator based on principles of classical component based software engineering. The architecture is directly derived from the project requirements and resides on the concept of an Autonomous Component utilizing a smart feedback value called WishLists. This return type is able to provide expert advice about the reasons of occurring failures and give hints for possible recovery strategies. This is of key importance to advance towards robustness. The integration of an AI task planner allows the realization of higher flexibility, dependability and capability during task execution and may resolve conflicts between occurring WishLists. Furthermore the necessity of a central system-state model (Eigenmodel), which represents the current state and configuration of the whole system at runtime, is explained and illustrated. We conclude with some lessons learned.
With regard to performance well established SW-only design methodologies proceed by making the initial specification run first, then by enhancing its functionality and finally by optimizing it. When designing Embedded Systems (EbS) this approach is not viable since decisive design decisions like e.g. the estimation of required processing power or the identification of those parts of the specification which need to be delegated to dedicated HW depend on the fastness and fairness of the initial specification. We here propose a sequence of optimization steps embedded into the design flow, which enables a structured way to accelerate a given working EbS specification at different layers. This sequence of accelerations comprises algorithm selection, algorithm transformation, data transformation, implementation optimization and finally HW acceleration. It is analyzed how all acceleration steps are influenced by the specific attributes of the underlying EbS. The overall acceleration procedure is explained and quantified at hand of a real-life industrial example.
Co-design is concerned with the joint design of hardware and software making up an embedded computer system [Wol94]. A top down design flow for an embedded system begins with a system specification. If it is executable, it may be used for simulation, system verification or to identify algorithmical bottlenecks. In contrast to other chapters of this book, the specification is not developed in this case study, rather it is given from the beginning. Furthermore we are not concerned with partitioning or synthesis of dedicated HW. Instead we focus on the problem how to find an off-the-shelf micro-controller which implements the desired functionality and meets all specification constraints. If feasible, this is usually much cheaper then using dedicated hardware. This chapter will answer the question of feasibility for a real life problem from automobile industry.
Improving Robustness of Task Execution Against External Faults Using Simulation Based Approach
(2013)
Robots interacting in complex and cluttered environments may face unexpected situations referred to as external faults which prohibit the successful completion of their tasks. In order to function in a more robust manner, robots need to recognise these faults and learn how to deal with them in the future. We present a simulation-based technique to avoid external faults occurring during execusion releasing actions of a robot. Our technique utilizes simulation to generate a set of labeled examples which are used by a histogram algorithm to compute a safe region. A safe region consists of a set of releasing states of an object that correspond to successful performances of the action. This technique also suggests a general solution to avoid the occurrence of external faults for not only the current, observable object but also for any other object of the same shape but different size.
We developed a scene text recognition system with active vision capabilities, namely: auto-focus, adaptive aperture control and auto-zoom. Our localization system is able to delimit text regions in images with complex backgrounds, and is based on an attentional cascade, asymmetric adaboost, decision trees and Gaussian mixture models. We think that text could become a valuable source of semantic information for robots, and we aim to raise interest in it within the robotics community. Moreover, thanks to the robot’s pan-tilt-zoom camera and to the active vision behaviors, the robot can use its affordances to overcome hindrances to the performance of the perceptual task. Detrimental conditions, such as poor illumination, blur, low resolution, etc. are very hard to deal with once an image has been captured and can often be prevented. We evaluated the localization algorithm on a public dataset and one of our own with encouraging results. Furthermore, we offer an interesting experiment in active vision, which makes us consider that active sensing in general should be considered early on when addressing complex perceptual problems in embodied agents.
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.
In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.
In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution.
In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model.
In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
Finding good Echo State Networks to control an underwater robot using evolutionary computations
(2004)
The control of an underwater robot is difficult due to non-linear effects. Echo State Networks (ESNs) provide a way of dealing with non-linearity. The quality of an Echo State Network (ESN) (H. Jaeger, 2001, 2002) depends strongly on its topology. Usually an educated guess combined with a brute force method is used to obtain an ESN that, after training, produces a low error. In this article the authors suggest another way to find good topologies by using evolution. Two heuristics, evolutionary algorithms and evolutionary strategies, are compared. The proposed method outperforms standard ones like ARX and backpropagation networks on the data from the Twin-Burger underwater robot.
Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.
Vision-based motion detection, an important skill for an autonomous mobile robot operating in dynamic environments, is particularly challenging when the robot's camera is in motion. In this paper, we use a Fourier-Mellin transform-based image registration method to compensate for camera motion before applying temporal differencing for motion detection. The approach is evaluated online as well as offline on a set of sequences recorded with a Care-O-bot 3, and compared with a feature-based method for image registration. In comparison to the feature-based method, our method performs better both in terms of robustness of the registration and the false discovery rate.
GMD-Robots
(2002)
A way of combining a relatively new sensor-technology, that is optical analog VLSI devices, with a standard digital omni-directional vision system is investigated. The sensor used is a neuromorphic analog VLSI sensor that estimates the global visual image motion. The sensor provides two analog output voltages that represent the components of the global optical flow vector. The readout is guided by an omni-directional mirror that maps the location of the ball and directs the robot to align its position so that a sensor-actuator module that includes the analog VLSI optical flow sensor can be activated. The purpose of the sensor-actuator module is to operate with a higher update rate than the standard vision system and thus increase the reactivity of the robot for very specific situations. This paper will demonstrate an application example where the robot is a goalkeeper with the task of defending the goal during a penalty kick.
Robots, which are able to carry out their tasks robustly in real world environments, are not only desirable but necessary if we want them to be more welcome for a wider audience. But very often they may fail to execute their actions successfully because of insufficient information about behaviour of objects used in the actions.
GMD-Robots
(2001)
