Grasping objects in a smooth humanlike motion, instead of the more typical pick-and-place approach, includes multiple aspects that need to be performed correctly for a successful grasp. These aspects involve moving the end-effector such that its surface makes and retains contact with the object while also coordinating the movement of the gripper to securely grasp the object. This work investigates how the intricate task of grasping may be learned from humans based on kinesthetic demonstrations. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e. end-effector movement, orientation and gripper width) have to be demonstrated at once. Rather than training a person to provide faster demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task.
The presented framework learns the desired movement dynamics based on the current Cartesian position with Gaussian Processes, resulting in a reactive, time-invariant policy. Using Gaussian Processes also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization.
The experimental evaluation of the framework is carried out on a Franka-Emika Panda. Tests were performed to determine i) the framework's effectiveness in successfully learning how to grasp an object quickly, ii) ease of policy correction to environmental changes (i.e. different object shapes and mass), and iii) the framework’s usability for non-expert users.