Task management

Résumé

Task management is the process of managing a task through its lifecycle. It involves planning, testing, tracking, and reporting. Task management can help either individual achieve goals, or groups of individuals collaborate and share knowledge for the accomplishment of collective goals. Tasks are also differentiated by complexity, from low to high. Effective task management requires managing all aspects of a task, including its status, priority, time, human and financial-resources assignments, recurrence, dependency, notifications,etc. These can be lumped together broadly into the basic activities of task management. Managing multiple individuals or team tasks may be assisted by specialized software, for example workflow or project-management software, and such software may sometimes be referred to a productivity system. Task management may form part of project management and process management, and can serve as the foundation for efficient workflow in

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https://en.wikipedia.org/wiki/Task_management

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Evolving Monolithic Robot Controllers through Incremental Shaping

Joshua Evan Auerbach

Evolutionary robotics has been shown to be an effective technique for generating robot behaviors that are difficult to derive analytically from the robot’s mechanics and task environment. Moreover, augmenting evolutionary algorithms with environmental scaffolding via an incremental shaping method makes it possible to evolve controllers for complex tasks that would otherwise be infeasible. In this paper we present a summary of two recent publications in the evolutionary robotics literature demonstrating how these methods can be used to evolve robot controllers for non-trivial tasks, what the obstacles are in evolving controllers in this way, and present a novel research question that can be investigated under this framework.

Springer Berlin Heidelberg2011

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How robot morphology and training order affect the learning of multiple behaviors

Joshua Evan Auerbach

Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic motion of different parts of the body at different times. This paper presents an incremental shaping method that addresses this challenge: it trains a controller to both coordinate a robot's leg motions to achieve directed locomotion toward an object, and then coordinate gripper motion to achieve lifting once the object is reached. It is shown that success is dependent on the order in which these behaviors are learned, and that despite the fact that one robot can master these behaviors better than another with a different morphology, this learning order is invariant across the two robot morphologies investigated here. This suggests that aspects of the task environment, learning algorithm or the controller dictate learning order more than the choice of morphology.

IEEE2009

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On the Relationship Between Environmental and Mechanical Complexity in Evolved Robots

Joshua Evan Auerbach

According to the principles of embodied cognition, intelligent behavior must arise out of the coupled dynamics of an agent's brain, body, and environment. This suggests that the morphological complexity of a robot should scale in relation to the complexity of its task environment. This idea is supported by recent work, which demonstrated that when evolving robot morphologies in simple and complex task environments more complex robot morphologies do tend to evolve in more complex task environments. Here this idea is extended to examining the mechanical complexity of evolved robots. Counter to intuition it is found that the mechanical complexity decreases in more complex task environments.

MIT Press2012

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