Robotik bir el üzerinde çevik manipülasyon

In robotics, flexible and the dexterous manipulation are one of the most desired type of skills. To this end, we investigate dexterous manipulation skills on an anthropomorphic robot hand. In the first part of the study, a sensorless grasping method is described. Although the high-precision sensing is highly relevant for precise grasps, precision is often not necessary to perform power grasps. An alternative approach is proposed for robotic grasping tasks based on external force estimation. Estimation accuracy is confirmed using a force sensor and the estimations are found to be useful for creating soft/power grasp behavior. In the second part, human-in-the-loop heterogeneous control for dexterous manipulation is investigated on a setup with a robotic hand and a robotic arm. The goal of the study is to experimentally verify that in tasks where the manual and explicit trajectory tuning is not possible, the autonomous movement can be learned by giving a basic policy to a robotic system, after which a human can learn and transfer an orthogonal complex part of the policy. The approach is shown on a ball swapping task in which a robotic arm is controlled by the human and a robotic hand is given an initial basic policy. In the results, we experimentally show that, in certain tasks, complex autonomous policies can be constructed by delegating the complex learning part to a human, the simple part to an autonomous agent, finally creating an autonomous control policy by recombining the parts.