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Found 6 papers, 2 papers with code
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware.
VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
We build upon the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different sequential fabric manipulation tasks with a single goal-conditioned policy.
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
We explore learning pixelwise correspondences between images of deformable objects in different configurations.
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics.
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery.
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone.
