This paper presents the technique of flex-and-flip manipulation. It is suitable for grasping thin, flexible linear objects lying on a flat surface. During the manipulation process, the object is first flexed by a robotic gripper whose fingers are placed on top of it, and later the increased internal energy of the object helps the gripper obtain a stable pinch grasp while the object flips into the space between the fingers. The dynamic interaction between the flexible object and the gripper is elaborated by analyzing how energy is exchanged. We also discuss the condition on friction to prevent loss of contact. Our flex-and-flip manipulation technique can be implemented with open-loop control and lends itself to underactuated, compliant finger mechanism. A set of experiments in robotic page turning performed with our customized hardware and software system demonstrates the effectiveness and robustness of the manipulation technique.

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Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn. Inspired by the human learning process, we propose a method to extract and exploit latent intents from demonstrations, and then learn diverse and robust grasping policies through selfexploration. The resulting policy can grasp challenging objects in various environments with an off-the-shelf parallel gripper.The key component is a learned intention estimator, which maps gripper pose and visual sensory to a set of sub-intents covering important phases of the grasping movement. Subintents can be used to build an intrinsic reward to guide policy learning. The learned policy demonstrates remarkable zero-shot generalization from simulation to the real world while retaining its robustness against states that have never been encountered during training, novel objects such as protractors and user manuals, and environments such as the cluttered conveyor.