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research:projects [2017/09/14 17:22]
c703101
research:projects [2018/09/07 11:29] (current)
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 ====== Externally-Funded,​ Collaborative Projects ====== ====== Externally-Funded,​ Collaborative Projects ======
  
-===== Current ​EU Projects =====+===== Current Projects =====
  
-{{:​research:​imagine-transparent.svg?​nolink&​200 ||}}[[https://​www.imagine-h2020.eu|IMAGINE ​Robots Understanding Their Actions by Imagining Their Effects ​]] (EU H20202017-2020): seeks to enable robots to understand the structure ​of their environment and how it is affected by its actions“Understanding” here means the ability ​of the robot (a) to determine ​the applicability ​of an action along with parameters to achieve the desired effectand (b) to discern ​to what extent an action succeeded, and to infer possible causes ​of failure ​and generate recovery actions.+{{:​research:​flexrop-logo.png?​nolink&​200 ||}}[[https://​www.profactor.at/​en/​research/​industrial-assistive-systems/​roboticassistance/​projects/​flexrop/​|FlexRoP ​Flexible, assistive robot for customized production]] (FFG (Austria) ICT of the Future2016-2018): Production ​of mass customized products ​is not easy to automate since objects and object positions remain more uncertain compared to mass production scenariosUncertainty handling motivates ​the application of advanced sensor-based control strategies which increases system complexity ​of robot applications dramatically. A possible solution ​to this conflict is the concept ​of task level or skill based programming that will render modern robot systems. Such systems can be applied without safety fenceare easier ​to programmore applicable ​and transformable into capable robot assistants. The project will implement a skill based programming framework and will apply it on selected industrial demo scenarios and evaluate research results. The main focus of the project is the application of methods to acquire process information by worker monitoring ​and thus make the robot assistants self-learning.
  
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 +{{:​research:​imagine-transparent.png?​nolink&​200 ||}}[[https://​www.imagine-h2020.eu|IMAGINE - Robots Understanding Their Actions by Imagining Their Effects ]] (EU H2020, 2017-2020): seeks to enable robots to understand the structure of their environment and how it is affected by its actions. “Understanding” here means the ability of the robot (a) to determine the applicability of an action along with parameters to achieve the desired effect, and (b) to discern to what extent an action succeeded, and to infer possible causes of failure and generate recovery actions.
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 +===== Completed Projects (Selection) =====
  
 {{:​research:​squirrel.png?​nolink&​200 |}}[[http://​www.squirrel-project.eu/​|SQUIRREL]] (EU FP7-ICT-STREP,​ 2014-2018): Clutter in an open world is a challenge for many aspects of robotic systems, especially for autonomous robots deployed in unstructured domestic settings, affecting navigation, manipulation,​ vision, human robot interaction and planning. ​ SQUIRREL addresses these issues by actively controlling clutter and incrementally learning to extend the robot'​s capabilities while doing so. We term this the B3 (bit by bit) approach, as the robot tackles clutter one bit at a time and also extends its knowledge continuously as new bits of information become available. ​ SQUIRREL is inspired by a user driven scenario, that exhibits all the rich complexity required to convincingly drive research, but allows tractable solutions with high potential for exploitation. We propose a toy cleaning scenario, where a robot learns to collect toys scattered in loose clumps or tangled heaps on the floor in a child'​s room, and to stow them in designated target locations. {{:​research:​squirrel.png?​nolink&​200 |}}[[http://​www.squirrel-project.eu/​|SQUIRREL]] (EU FP7-ICT-STREP,​ 2014-2018): Clutter in an open world is a challenge for many aspects of robotic systems, especially for autonomous robots deployed in unstructured domestic settings, affecting navigation, manipulation,​ vision, human robot interaction and planning. ​ SQUIRREL addresses these issues by actively controlling clutter and incrementally learning to extend the robot'​s capabilities while doing so. We term this the B3 (bit by bit) approach, as the robot tackles clutter one bit at a time and also extends its knowledge continuously as new bits of information become available. ​ SQUIRREL is inspired by a user driven scenario, that exhibits all the rich complexity required to convincingly drive research, but allows tractable solutions with high potential for exploitation. We propose a toy cleaning scenario, where a robot learns to collect toys scattered in loose clumps or tangled heaps on the floor in a child'​s room, and to stow them in designated target locations.
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-===== Completed Projects (Selection) ===== 
  
 {{:​research:​pacman_logo2.png?​nolink&​110 |PaCMan}} [[http://​www.pacman-project.eu/​|PaCMan]] (EU FP7-ICT-STREP,​ 2013-2016) advances methods for object perception, representation and manipulation so that a robot is able to robustly manipulate objects even when those objects are unfamiliar, and even though the robot has unreliable perception and action. The proposal is founded on two assumptions. The first of these is that the representation of the object'​s shape in particular and of other properties in general will benefit from being compositional (or very loosely hierarchical and part based). The second is that manipulation planning and execution benefits from explicitly reasoning about uncertainty in object pose, shape etcetera; how it changes under the robot'​s actions, and the robot should plan actions that not only achieve the task, but gather information to make task achievement more reliable. {{:​research:​pacman_logo2.png?​nolink&​110 |PaCMan}} [[http://​www.pacman-project.eu/​|PaCMan]] (EU FP7-ICT-STREP,​ 2013-2016) advances methods for object perception, representation and manipulation so that a robot is able to robustly manipulate objects even when those objects are unfamiliar, and even though the robot has unreliable perception and action. The proposal is founded on two assumptions. The first of these is that the representation of the object'​s shape in particular and of other properties in general will benefit from being compositional (or very loosely hierarchical and part based). The second is that manipulation planning and execution benefits from explicitly reasoning about uncertainty in object pose, shape etcetera; how it changes under the robot'​s actions, and the robot should plan actions that not only achieve the task, but gather information to make task achievement more reliable.
research/projects.1505402563.txt.gz · Last modified: 2018/09/03 14:57 (external edit)