Transferring Manipulative Skills to Robots: Representation and Acquisition of Tool Manipulative Skills Using a Process Dynamics Model

Abstract

A new method based on task process models for acquiring manipulative skills from human experts is presented. In performing manipulative tasks such as deburring, a human expert moves a tool at an optimal feedrate and cutting force as well as with an appropriate compliance for holding the tool. An experienced worker can select the correct strategy for performing a task and change it dynamically in accordance with the task process state. In this paper, the human expertise for selecting a task strategy that accords with the process characteristics is modeled as an associative mapping, and represented and generated by using a neural network. First, the control strategy for manipulating a tool is described in terms of feedforward inputs and tool holding dynamics. The parameters and variables representing the control strategy are then identified by using teaching data taken from demonstrations by an expert. The task process is also modeled and characterized by a set of parameters, which are identified by using this same teaching data. Combining the two sets of identified parameters, we can derive an associative mapping from the task process characteristics to the task strategy parameters. The consistency of the mapping and the transferability of human skills are analyzed by using Lipschitz’s condition. The method is applied to deburring, and implemented on a direct-drive robot. It is shown that the robot is able to associate a correct control strategy with process characteristics in a manner similar to that of the human expert.

Bio-mimetic impedance control of robotic manipulator for dynamic contact tasks

“…Finally, computer simulations are conducted to reproduce human hand movements in the ball-catching task using estimated task-readiness impedance and virtual trajectory, assuming that the dynamic characteristics of the human hand follow the impedance model given in (1). Fig.…”

“…Liu and Asada [1] pioneered this research area, and many other studies have followed their work [2][3][4][5][6]. Although these previous studies described the characteristics of human movements in the framework of an impedance model, they did not investigate human impedance properties involved in the tasks.…”

Quantitative Evaluation for Skill Controller Based on Comparison With Human Demonstration

“…must be specified for one of the output variables, i.e., the position signal, the force signal, or the feature value of the tool acquired by CCD camera. Thus, the design of the event observer can be formulated as the optimization problem of these parameters, and expressed by Find EO which minimizes (13) where and represent the HMM whose construction is based on the profile of human's working data and the robot's working data in which the EO is implemented, respectively. denotes the distance between and as formulated in (1).…”

Towards gesture-based programming: shape from motion primordial learning of sensorimotor primitives

“…Some researchers are applying supervised learning approaches to recover strategies for contact tasks like deburring. Liu and Asada [15] use a neural network to recover an associative mapping representing the human skill in performing a deburring task. The task is performed using a direct-drive robot with low friction and a force sensor integrated with the workpiece.…”