Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Physical workload and musculoskeletal symptoms in the human-horse work environment

Most work in horse stables is performed manually in much the same way as a century ago, with old-fashioned tools and equipment. It is one of the least mechanised sectors dealing with large animals, which often involves work in awkward postures and lifts of heavy loads. However, there is a lack of knowledge of the ergonomic risks in the human-horse work environment. This thesis seeks to provide a deeper understanding of the human-horse work environment, work tasks, workload and frequency of musculoskeletal symptoms and to identify potential risk factors for the development of musculoskeletal symptoms. Self-reporting methods (questionnaires, rating scales), observation methods (OWAS, REBA), descriptive task analysis (HTA, HA, GTS) and biomechanical analysis (JACK) were used to collect and analyse data. Riding instructors surveyed in the questionnaire study reported high levels of perceived musculoskeletal symptoms in at least one of nine anatomical areas during the past year and the past week. The most frequently reported problem areas were the shoulders, the lower back and the neck. Mucking out stables was considered to be the task involving the heaviest work. OWAS analysis showed that three work tasks contained a high proportion of unacceptably awkward work postures, namely mucking out, preparing bedding and sweeping. During mucking out and sweeping, the back was bent and twisted for most of the time. There were many high-risk operations involved in mucking out boxes and disposing of bedding material. Emptying a wheel barrow on the muck heap included high-risk operations with awkward postures such as twisted, bent back arms over shoulder level and handling high loads. The analytical methods used clearly revealed where in the work tasks the ergonomic problems occurred. In almost all operations with a high risk level, a shafted tool or wheelbarrow was used. Analysis of the shaft length of two hand-held tools used for mucking out (manure fork, shavings fork) showed that the manure fork should have a longer shaft to reduce loading on the back. The results for the shavings fork were inconclusive, but indicated the importance of changes in work technique. More in-depth knowledge of the musculoskeletal symptoms and work tasks performed in the human-horse work environment makes it easier to plan and implement measures to prevent musculoskeletal symptoms in this particular group of workers

Acceleration of Computational Geometry Algorithms for High Performance Computing Based Geo-Spatial Big Data Analysis

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that it can run in parallel on big computers with massive processing power and hence reduce the computing time delivering the same results but much faster.This dissertation explores different techniques to accelerate the processing of computational geometry algorithms and geo-spatial computing like using Many-core Graphics Processing Units (GPU), Multi-core Central Processing Units (CPU), Multi-node setup with Message Passing Interface (MPI), Cache optimizations, Memory and Communication optimizations, load balancing, Algorithmic Modifications, Directive based parallelization with OpenMP or OpenACC and Vectorization with compiler intrinsic (AVX). This dissertation has applied at least one of the mentioned techniques to the following problems. Novel method to parallelize plane sweep based geometric intersection for GPU with directives is presented. Parallelization of plane sweep based Voronoi construction, parallelization of Segment tree construction, Segment tree queries and Segment tree-based operations has been presented. Spatial autocorrelation, computation of getis-ord hotspots are also presented. Acceleration performance and speedup results are presented in each corresponding chapter

Proceedings of the First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014): Porto, Portugal

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014

Creating music by listening

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 127-139).Machines have the power and potential to make expressive music on their own. This thesis aims to computationally model the process of creating music using experience from listening to examples. Our unbiased signal-based solution models the life cycle of listening, composing, and performing, turning the machine into an active musician, instead of simply an instrument. We accomplish this through an analysis-synthesis technique by combined perceptual and structural modeling of the musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis intends to be generic and uniform by recursively revealing metrical hierarchies and structures of pitch, rhythm, and timbre. Training is suggested for top-down un-biased supervision, and is demonstrated with the prediction of downbeat. This musical intelligence enables a range of original manipulations including song alignment, music restoration, cross-synthesis or song morphing, and ultimately the synthesis of original pieces.by Tristan Jehan.Ph.D