research:projects
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research:projects [2019/07/15 09:38] Justus Piater [Current Projects] |
research:projects [2019/10/29 07:41] Simon Haller-Seeber |
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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. | + | {{:research:imagine-transparent.png?nolink&200 ||}}[[https://www.imagine-h2020.eu|IMAGINE - Robots Understanding Their Actions by Imagining Their Effects ]] (EU H2020, 2017-2021): 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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| - | {{: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}} [[https://cordis.europa.eu/project/rcn/106859/en|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. |
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research/projects.txt · Last modified: 2024/02/19 12:24 by Antonio Rodriguez-Sanchez