Parametrization of Robotic Deburring Process with Motor Skills from Motion Primitives of Human Skill Model

Date

2017-08-31

Author

Parvizi, Payam
Ugurlu, Musab Cagri
Açıkgöz, Kemal
Konukseven, Erhan İlhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

212
views

0
downloads

In this paper, a new method for learning robotic deburring process motor skills from human skill model is presented. The skill of a human expert is obtained by using the human skill model. This model is implemented in robot control systems that make robots imitate human skills in deburring process tasks. Based on deburring process, family of basic pattern motions are listed by identifying the movement in the task (e.g. circular, straight line and sharp corners). These motions depend on a number of complex parameters; therefore, it is hard to fit a completely mathematical model to the process. By inspecting the movements of the human expert in task and analyzing these movements, the dynamic movement primitives are examined using an ordinary differential equation. In this paper, the standard method of Dynamic Movement Primitives (DMP) that would help extracting the trajectory for human interaction behaviors without using its explicit model, is modified to extract human expertise to provide a more accurate approximation of the robotic deburring process tasks. The experiments are conducted on haptic device, in which the robotic deburring processes on workpiece are imitated and the results are used as primitives to accomplish automatic deburring on the workpiece.

URI

https://hdl.handle.net/11511/54378

Collections

Department of Mechanical Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Demonstration of robotic deburring process motor skills from motion primitives of human skill model Parvizi, Payam; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2018) This study presents a new method to learn motor skills of robotic deburring process from a human expert who is performing manual deburring operation. By utilizing this method, it is possible to automate the robotic deburring process of a workpiece with an unknown shape. In this work, the task-related movements of the human expert are recorded using 6DOF and 1DOF haptic devices. Then, the intrinsic movement primitives are parametrized by using Dynamic Movement Primitive (DMP) method. By collecting dataset of...
Towards an on-line neural conditioning model for mobile robots Şahin, Erol (2001-01-01) This paper presents a neural conditioning model for on-line learning of behaviors on mobile robots. The model is based on Grossberg's neural model of conditioning as recently implemented by Chang and Gaudiano. It attempts to tackle some of the limitations of the original model by (1) using a temporal difference of the reinforcement to drive learning, (2) adding eligibility trace mechanisms to dissociate behavior generation from learning, (3) automatically categorizing sensor readings and (4) bootstrapping t...
Simple and complex behavior learning using behavior hidden Markov model and CobART Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Yavaş, Mustafa (2013-03-01) This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categoriz...
Simple and complex behavior learning using behavior hidden Markov Model and CobART Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Department of Computer Engineering (2013) In this thesis, behavior learning and generation models are proposed for simple and complex behaviors of robots using unsupervised learning methods. Simple behaviors are modeled by simple-behavior learning model (SBLM) and complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models have common phases named behavior categorization, behavior modeling, and behavior generation. Sensory data are categorized using correlation based...
Free gait generation with reinforcement learning for a six-legged robot Erden, Mustafa Suphi; Leblebicioğlu, Mehmet Kemal (Elsevier BV, 2008-03-31) In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no ext...

Citation Formats

P. Parvizi, M. C. Ugurlu, K. Açıkgöz, and E. İ. Konukseven, “Parametrization of Robotic Deburring Process with Motor Skills from Motion Primitives of Human Skill Model,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54378.