Prolegomenon to the study of the concepts of maturity and maturity model : from Black Horse to White Knight?

This paper takes its root in a trivial observation: management approaches are unable to provide relevant guidelines to cope with uncertainty, and trust of our modern worlds. Thus, managers are looking for reducing uncertainty through information’s supported decision-making, sustained by ex-ante rationalization. They strive to achieve best possible solution, stability, predictability, and control of “future”. Hence, they turn to a plethora of “prescriptive panaceas”, and “management fads” to bring simple solutions through best practices. However, these solutions are ineffective. They address only one part of a system (e.g. an organization) instead of the whole. They miss the interactions and interdependencies with other parts leading to “suboptimization”. Further classical cause-effects investigations and researches are not very helpful to this regard. Where do we go from there? In this conversation, we want to challenge the assumptions supporting the traditional management approaches and shed some lights on the problem of management discourse fad using the concept of maturity and maturity models in the context of temporary organizations as support for reflexion. Global economy is characterized by use and development of standards and compliance to standards as a practice is said to enable better decision-making by managers in uncertainty, control complexity, and higher performance. Amongst the plethora of standards, organizational maturity and maturity models hold a specific place due to general belief in organizational performance as dependent variable of (business) processes continuous improvement, grounded on a kind of evolutionary metaphor. Our intention is neither to offer a new “evidence based management fad” for practitioners, nor to suggest research gap to scholars. Rather, we want to open an assumption-challenging conversation with regards to main stream approaches (neo-classical economics and organization theory), turning “our eyes away from the blinding light of eternal certitude towards the refracted world of turbid finitude” (Long, 2002, p. 44) generating what Bernstein has named “Cartesian Anxiety” (Bernstein, 1983, p. 18), and revisit the conceptualization of maturity and maturity models. We rely on conventions theory and a systemic-discursive perspective. These two lenses have both information & communication and self-producing systems as common threads. Furthermore the narrative approach is well suited to explore complex way of thinking about organizational phenomena as complex systems. This approach is relevant with our object of curiosity, i.e. the concept of maturity and maturity models, as maturity models (as standards) are discourses and systems of regulations. The main contribution of this conversation is that we suggest moving from a neo-classical “theory of the game” aiming at making the complex world simpler in playing the game, to a “theory of the rules of the game”, aiming at influencing and challenging the rules of the game constitutive of maturity models – conventions, governing systems – making compatible individual calculation and social context, and possible the coordination of relationships and cooperation between agents with or potentially divergent interests and values. A second contribution is the reconceptualization of maturity as structural coupling between conventions, rather than as an independent variable leading to organizational performance.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.