Workspace | EPFL Graph Search

Workspace is a term used in various branches of engineering and economic development. Workspace refers to small premises provided, often by local authorities or economic development agencies, to help new businesses to establish themselves. These typically provide not only physical space and utilities but also administrative services and links to support and finance organizations, as well as peer support among the tenants. A continuum of sophistication ranges through categories such as 'managed workspaces', 'business incubators' and 'business and employment co-operatives'. In cities, they are often set up in buildings that are disused but which the local authority wishes to retain as a landmark. At the larger end of the spectrum are business parks, virtual offices, technology parks and science parks. In technology and software, "workspace" is a term used for several different purposes. A workspace is (often) a or that allows a user to gather various source code files and resources and work with them as a cohesive unit. Often these files and resources represent the complete state of an integrated development environment (IDE) at a given time, a snapshot. Workspaces are very helpful in cases of complex projects when maintenance can be challenging. Good examples of environments that allow users to create and use workspaces are Microsoft Visual Studio and Eclipse. In configuration management, "workspace" takes on a different but related meaning; it is a part of the file system where the files of interest (for a given task like debugging, development, etc.) are located. It stores the user's view of the files stored in the configuration management's repository. In either case, workspace acts as an environment where a programmer can work, isolated from the outside world, for the task duration. Virtual desktop Additionally, workspaces refer to the grouping of windows in some window managers. Grouping applications in this way is meant to reduce clutter and make the desktop easier to navigate.

Learning to Hit: A statistical Dynamical System based approach

Aude Billard, Michael Bosongo Bombile, Harshit Khurana

This paper proposes a manipulation scheme based on learning the motion of objects after being hit by a robotic end-effector. This allows for the object to be positioned at a desired location outside the physical workspace of the robot. An estimate of the object dynamics under friction and collisions is learnt and used to predict the desired hitting parameters (speed and direction), given the initial and desired location of the object. Based on the obtained hitting parameters, the desired pre-impact velocity of the end-effector is generated using a stable dynamical system. The performance of the proposed DS is validated in simulation and and is used to learn a model for hitting using real robot. The approach is tested on real robot with a KUKA LBR IIWA robot.

IEEE2021

Preliminary bridge design navigation tool for novices

The motivation of the thesis comes from the frustrations of young engineers confronted with real design problems. The inspiration of the thesis evolved from observations of bridge designers and analyses of bridge design competitions. Not only do designers adopt more than one strategy during design, they rarely perform a fixed sequence of tasks. Not only do designers consider more than one criterion during design, their priorities shift during the determination of parameters. The choice of task sequence, which is largely reflected in the style of the designer, is considered a major source of design variety. The thesis is founded on the premise that design is path dependent. Hence, freedom of sequence and support for an opportunistic strategy, are essential aspects to acquire, model and implement, in order to capture realistic design situations, to achieve design variety for the same initial conditions and to apply these aspects to interactive computational environments. In this thesis, an integrated approach was adopted throughout acquisition, modelling and implementation, in order to propose formalisms, a framework and a design tool that supports freedom of sequence. The formalisms consist of four design characteristics (tasks, parameters, strategies and criteria), a decision making process (the labyrinth metaphor) and a control process (design navigation). The style of the designer is derived as the strategies and criteria adopted for making decisions about tasks and parameters. The labyrinth metaphor is considered the most realistic reflection of decision making paths observed with designers. Design navigation highlights a designer's skill for controlling the labyrinth, without getting lost. However, more acquisition is needed to strengthen the strategies proposed. The framework integrates these formalisms into four parts: a design space, a workspace, a decision path and design interactions. The notion of a workspace, as a continually changing subset of the design space, prepares the labyrinth metaphor for implementation into the interface. Design interactions model the interactions between the design space and the workspace, for navigating in the labyrinth of tasks and parameters. However, a more extensive design space is still required for improving the framework's functionalities, in particular for interacting with CAD tools. The design tool's interface is an implementation of the framework. The interface rests on two dynamic window environments called the task workspace and the parameter workspace. Each workspace has a labyrinth area and a navigation area, which separates decision making from control, explicitly. A prototype of the design tool, called FIBRES, was demonstrated to practitioners and students, whose feedback confirmed the importance of allowing the user freedom of sequence and control over interactions. However, a more robust prototype is needed to perform extensive testing with students. A user-interface that accommodates unrestricted task sequencing has the potential to train novice designers in good bridge design by inducing an implicit mode of learning, and to provide experienced designers with computer workspaces compatible with their own decision making style. In the long term, such an approach may contribute to a better acceptance and penetration of artificial intelligence techniques into civil engineering education and practice.

EPFL1997