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In this paper, we present Infinite Latent Conditional Random Fields (ILCRFs) that model the data through a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the data. In addition to visible nodes and edges that exist in classic CRFs, it generatively models the distribution of different CRF structures over the latent nodes and corresponding edges, imposing...
Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane...
The ability to place objects in an environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred location/orientation. For instance, it is preferred that a plate be inserted vertically into the slot of a dish-rack as compared to being placed horizontally in it. Unstructured environments such as homes have a large variety...
Given an image and an aligned depth map of an object, our goal is to estimate the full 7-dimensional gripper configuration—its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects—ones not seen by the robot before. While these approaches use low-dimensional representations such as a ‘grasping point’ or a ‘pair...
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