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Found 5 papers, 2 papers with code
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.
Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts
This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i. e. direct human-robot contact over the full robot body.
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.
