Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Visual attention in the real world

Humans typically direct their gaze and attention at locations important for the tasks they are engaged in. By measuring the direction of gaze, the relative importance of each location can be estimated which can reveal how cognitive processes choose where gaze is to be directed. For decades, this has been done in laboratory setups, which have the advantage of being well-controlled. Here, visual attention is studied in more life-like situations, which allows testing ecological validity of laboratory results and allows the use of real-life setups that are hard to mimic in a laboratory. All four studies in this thesis contribute to our understanding of visual attention and perception in more complex situations than are found in the traditional laboratory experiments. Bottom-up models of attention use the visual input to predict attention or even the direction of gaze. In such models the input image is analyzed for each of several features first. In the classic Saliency Map model, these features are color contrast, luminance contrast and orientation contrast. The “interestingness” of each location in the image is represented in a ‘conspicuity maps’, one for each feature. The Saliency Map model then combines these conspicuity maps by linear addition, and this additivity has recently been challenged. The alternative is to use the maxima across all conspicuity maps. In the first study, the features color contrast and luminance contrast were manipulated in photographs of natural scenes to test which of these mechanisms is the best predictor of human behavior. It was shown that a linear addition, as in the original model, matches human behavior best. As all the assumptions of the Saliency Map model on the processes preceding the linear addition of the conspicuity maps are based on physiological research, this result constrains future models in their mechanistic assumption. If models of visual attention are to have ecological validity, comparing visual attention in laboratory and real-world conditions is necessary, and this is done in the second study. In the first condition, eye movements and head-centered, first-person perspective movies were recorded while participants explored 15 real-world environments (“free exploration”). Clips from these movies were shown to participants in two laboratory tasks. First, the movies were replayed as they were recorded (“video replay”), and second, a shuffled selection of frames was shown for 1 second each (“1s frame replay”). Eye-movement recordings from all three conditions revealed that in comparison to 1s frame replay, the video replay condition was qualitatively more alike to the free exploration condition with respect to the distribution of gaze and the relationship between gaze and model saliency and was quantitatively better able to predict free exploration gaze. Furthermore, the onset of a new frame in 1s frame replay evoked a reorientation of gaze towards the center. That is, the event of presenting a stimulus in a laboratory setup affects attention in a way unlikely to occur in real life. In conclusion, video replay is a better model for real-world visual input. The hypothesis that walking on more irregular terrain requires visual attention to be directed at the path more was tested on a local street (“Hirschberg”) in the third study. Participants walked on both sides of this inclined street; a cobbled road and the immediately adjacent, irregular steps. The environment and instructions were kept constant. Gaze was directed at the path more when participants walked on the steps as compared to the road. This was accomplished by pointing both the head and the eyes lower on the steps than on the road, while only eye-in-head orientation was spread out along the vertical more on the steps, indicating more or large eye movements on the more irregular steps. These results confirm earlier findings that eye and head movements play distinct roles in directing gaze in real-world situations. Furthermore, they show that implicit tasks (not falling, in this case) affect visual attention as much as explicit tasks do. In the last study it is asked if actions affect perception. An ambiguous stimulus that is alternatively perceived as rotating clockwise or counterclockwise (the ‘percept’) was used. When participants had to rotate a manipulandum continuously in a pre-defined direction – either clockwise or counterclockwise – and reported their concurrent percept with a keyboard, percepts weren’t affected by movements. If participants had to use the manipulandum to indicate their percept – by rotating either congruently or incongruently with the percept – the movements did affect perception. This shows that ambiguity in visual input is resolved by relying on motor signals, but only when they are relevant for the task at hand. Either by using natural stimuli, by comparing behavior in the laboratory with behavior in the real world, by performing an experiment on the street, or by testing how two diverse but everyday sources of information are integrated, the faculty of vision was studied in more life like situations. The validity of some laboratory work has been examined and confirmed and some first steps in doing experiments in real-world situations have been made. Both seem to be promising approaches for future research

Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated. This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively. Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Representation and control of coordinated-motion tasks for human-robot systems

It is challenging for robots to perform various tasks in a human environment. This is because many human-centered tasks require coordination in both hands and may often involve cooperation with another human. Although human-centered tasks require different types of coordinated movements, most of the existing methodologies have focused only on specific types of coordination. This thesis aims at the description and control of coordinated-motion tasks for human-robot systems; i.e., humanoid robots as well as multi-robot and human-robot systems. First, for bimanually coordinated-motion tasks in dual-manipulator systems, we propose the Extended-Cooperative-Task-Space (ECTS) representation, which extends the existing Cooperative-Task-Space (CTS) representation based on the kinematic models for human bimanual movements in Biomechanics. The proposed ECTS representation can represent the whole spectrum of dual-arm motion/force coordination using two sets of ECTS motion/force variables in a unified manner. The type of coordination can be easily chosen by two meaningful coefficients, and during coordinated-motion tasks, each set of variables directly describes two different aspects of coordinated motion and force behaviors. Thus, the operator can specify coordinated-motion/force tasks more intuitively in high-level descriptions, and the specified tasks can be easily reused in other situations with greater flexibility. Moreover, we present consistent procedures of using the ECTS representation for task specifications in the upper-body and lower-body subsystems of humanoid robots in order to perform manipulation and locomotion tasks, respectively. Besides, we propose and discuss performance indices derived based on the ECTS representation, which can be used to evaluate and optimize the performance of any type of dual-arm manipulation tasks. We show that using the ECTS representation for specifying both dual-arm manipulation and biped locomotion tasks can greatly simplify the motion planning process, allowing the operator to focus on high-level descriptions of those tasks. Both upper-body and lower-body task specifications are demonstrated by specifying whole-body task examples on a Hubo II+ robot carrying out dual-arm manipulation as well as biped locomotion tasks in a simulation environment. We also present the results from experiments on a dual-arm robot (Baxter) for teleoperating various types of coordinated-motion tasks using a single 6D mouse interface. The specified upper- and lower-body tasks can be considered as coordinated motions with constraints. In order to express various constraints imposed across the whole-body, we discuss the modeling of whole-body structure and the computations for robotic systems having multiple kinematic chains. Then we present a whole-body controller formulated as a quadratic programming, which can take different types of constraints into account in a prioritized manner. We validate the whole-body controller based on the simulation results on a Hubo II+ robot performing specified whole-body task examples with a number of motion and force constraints as well as actuation limits. Lastly, we discuss an extension of the ECTS representation, called Hierarchical Extended-Cooperative-Task Space (H-ECTS) framework, which uses tree-structured graphical representations for coordinated-motion tasks of multi-robot and human-robot systems. The H-ECTS framework is validated by experimental results on two Baxter robots cooperating with each other as well as with an additional human partner