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Found 6 papers, 2 papers with code
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation.
DITTO: Demonstration Imitation by Trajectory Transformation
Subsequently, in the live online trajectory generation stage, we first \mbox{re-detect} all objects, then we warp the demonstration trajectory to the current scene, and finally, we trace the trajectory with the robot.
PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
We frame this problem as the task of learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal.
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation
It consists of an RGB-D image encoder that leverages recent advances to detect objects and infer their pose and latent code, and a decoder to predict shape and grasps for each object in the scene.
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation.
Category-Independent Articulated Object Tracking with Factor Graphs
Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets.
