Visuotactile affordances for cloth manipulation with local control
Conference on Robot Learning, 2023•proceedings.mlr.press
Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key
regions, such as corners, are not directly graspable, making manipulation difficult. We
propose a system that leverages visual and tactile perception to unfold the cloth via grasping
and sliding on edges. Doing so, the robot is able to grasp two adjacent corners, enabling
subsequent manipulation tasks like folding or hanging. We develop tactile perception
networks that classify whether an edge is grasped and estimate the pose of the edge. We …
regions, such as corners, are not directly graspable, making manipulation difficult. We
propose a system that leverages visual and tactile perception to unfold the cloth via grasping
and sliding on edges. Doing so, the robot is able to grasp two adjacent corners, enabling
subsequent manipulation tasks like folding or hanging. We develop tactile perception
networks that classify whether an edge is grasped and estimate the pose of the edge. We …
Abstract
Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. Doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. We develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time.
proceedings.mlr.press
以上显示的是最相近的搜索结果。 查看全部搜索结果