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For robots acting - and failing - in everyday environments, a predictable behaviour representation is important so that it can be utilised for failure analysis, recovery, and subsequent improvement. Learning from demonstration combined with dynamic motion primitives is one commonly used technique for creating models that are easy to analyse and interpret; however, mobile manipulators complicate such models since they need the ability to synchronise arm and base motions for performing purposeful tasks. In this paper, we analyse dynamic motion primitives in the context of a mobile manipulator - a Toyota Human Support Robot (HSR)- and introduce a small extension of dynamic motion primitives that makes it possible to perform whole body motion with a mobile manipulator. We then present an extensive set of experiments in which our robot was grasping various everyday objects in a domestic environment, where a sequence of object detection, pose estimation, and manipulation was required for successfully completing the task. Our experiments demonstrate the feasibility of the proposed whole body motion framework for everyday object manipulation, but also illustrate the necessity for highly adaptive manipulation strategies that make better use of a robot's perceptual capabilities.
When developing robot functionalities, finite state machines are commonly used due to their straightforward semantics and simple implementation. State machines are also a natural implementation choice when designing robot experiments, as they generally lead to reproducible program execution. In practice, the implementation of state machines can lead to significant code repetition and may necessitate unnecessary code interaction when reparameterisation is required. In this paper, we present a small Python library that allows state machines to be specified, configured, and dynamically created using a minimal domain-specific language. We illustrate the use of the library in three different use cases - scenario definition in the context of the RoboCup@Home competition, experiment design in the context of the ROPOD project, as well as specification transfer between robots.
Background: Virtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.
New Method: We developed an optical flow-based method for tracking treadmil 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.
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 animal behavior across different scales was developed and characterized.
We present a novel, multilayer interaction approach that enables state transitions between spatially above-screen and 2D on-screen feedback layers. This approach supports the exploration of haptic features that are hard to simulate using rigid 2D screens. We accomplish this by adding a haptic layer above the screen that can be actuated and interacted with (pressed on) while the user interacts with on-screen content using pen input. The haptic layer provides variable firmness and contour feedback, while its membrane functionality affords additional tactile cues like texture feedback. Through two user studies, we look at how users can use the layer in haptic exploration tasks, showing that users can discriminate well between different firmness levels, and can perceive object contour characteristics. Demonstrated also through an art application, the results show the potential of multilayer feedback to extend on-screen feedback with additional widget, tool and surface properties, and for user guidance.
In the field of domestic service robots, recovery from 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. Even a well-modelled robot may fail to perform its tasks successfully due to external faults which occur because of an infinite number of unforeseeable and unmodelled situations. Through investigating the most frequent failures in typical scenarios which have been observed in real-world demonstrations and competitions using the autonomous service robots Care-O-Bot III and youBot, we identified four different fault classes caused by disturbances, imperfect perception, inadequate planning operator or chaining of action sequences. This thesis then presents two approaches to handle external faults caused by insufficient knowledge about the preconditions of the planning operator. The first approach presents reasoning on detected external faults using knowledge about naive physics. The naive physics knowledge is represented by the physical properties of objects which are formalized in a logical framework. The proposed approach applies a qualitative version of physical laws to these properties in order to reason. By interpreting the reasoning results the robot identifies the information about the situations which can cause the fault. Applying this approach to simple manipulation tasks like picking and placing objects show that naive physics holds great possibilities for reasoning on unknown external faults in robotics. The second approach includes missing knowledge about the execution of an action through learning by experimentation. Firstly, it investigates such representation of execution specific knowledge that can be learned for one particular situation and reused for situations which deviate from the original. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. This representation is called action execution model (AEM) here. The approach provides a learning strategy which uses a physical simulation for generating the training data to learn both symbolic and geometric aspects of the model. The experimental analysis, performed on two physical robots, shows that AEM can reliably describe execution specific knowledge and thereby serving as a potential model for avoiding the occurrence of external faults.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
Bond graph software can simulate bond graph models without the user needing to manually derive equations. This offers the power to model larger and more complex systems than in the past. Multibond graphs (those with vector bonds) offer a compact model which further eases handling multibody systems. Although multibond graphs can be simulated successfully, the use of vector bonds can present difficulties. In addition, most qualitative, bond graph–based exploitation relies on the use of scalar bonds. This article discusses the main methods for simulating bond graphs of multibody systems, using a graphical software platform. The transformation between models with vector and scalar bonds is presented. The methods are then compared with respect to both time and accuracy, through simulation of two benchmark models. This article is a tutorial on the existing methods for simulating three-dimensional rigid and holonomic multibody systems using bond graphs and discusses the difficulties encountered. It then proposes and adapts methods for simulating this type of system directly from its bond graph within a software package. The value of this study is in giving practical guidance to modellers, so that they can implement the adapted method in software.
Treatment options for acute myeloid leukemia (AML) remain extremely limited and associated with significant toxicity. Nicotinamide phosphoribosyltransferase (NAMPT) is involved in the generation of NAD+ and a potential therapeutic target in AML. We evaluated the effect of KPT-9274, a p21-activated kinase 4/NAMPT inhibitor that possesses a unique NAMPT-binding profile based on in silico modeling compared with earlier compounds pursued against this target. KPT-9274 elicited loss of mitochondrial respiration and glycolysis and induced apoptosis in AML subtypes independent of mutations and genomic abnormalities. These actions occurred mainly through the depletion of NAD+, whereas genetic knockdown of p21-activated kinase 4 did not induce cytotoxicity in AML cell lines or influence the cytotoxic effect of KPT-9274. KPT-9274 exposure reduced colony formation, increased blast differentiation, and diminished the frequency of leukemia-initiating cells from primary AML samples; KPT-9274 was minimally cytotoxic toward normal hematopoietic or immune cells. In addition, KPT-9274 improved overall survival in vivo in 2 different mouse models of AML and reduced tumor development in a patient-derived xenograft model of AML. Overall, KPT-9274 exhibited broad preclinical activity across a variety of AML subtypes and warrants further investigation as a potential therapeutic agent for AML.
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.