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research:grasp-densities [2012/06/19 15:47]
c7031007 created
research:grasp-densities [2012/06/19 18:56]
c7031007
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 ====== Interactive,​ Visuomotor Learning of Grasp Models ====== ====== Interactive,​ Visuomotor Learning of Grasp Models ======
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 +We are interested in probabilistic representations and learning methods that allow a robotic agent to infer useful behaviors from perceptual observations. We develop probabilistic models of 3D [[:​research:​mrf-object-models|visual]] and haptic object properties, along with means of learning the model autonomously from exploration:​ A robotic agent physically experiences the correlation between successful grasps and local visual appearance by “playing” with an object. With time, it becomes increasingly efficient at inferring grasp parameters from visual evidence. Our visuomotor object model relies on
 +  * a grasp model representing the grasp success likelihood of relative hand-object configurations,​ and
 +  * a 3D model of visual object structure, which aligns the grasp model to arbitrary object poses (3D positions and orientations).
 +These models are discussed below. ​
  
 We are working on the modeling and learning object grasp affordances,​ i.e. relative object-gripper poses that yield stable grasps. These affordances are represented probabilistically with grasp densities ([[@/​publications#​Detry-2011-PJBR|Detry et al. 2011]]), which correspond to continuous density functions defined on the space of 6D gripper poses – 3D position and orientation. We are working on the modeling and learning object grasp affordances,​ i.e. relative object-gripper poses that yield stable grasps. These affordances are represented probabilistically with grasp densities ([[@/​publications#​Detry-2011-PJBR|Detry et al. 2011]]), which correspond to continuous density functions defined on the space of 6D gripper poses – 3D position and orientation.
research/grasp-densities.txt · Last modified: 2018/09/03 19:35 (external edit)