IMITATION OF HUMAN BODY POSES BY THE FORMATION CONTROL OF A FLUIDIC SWARM

Date

2012-07-04

Author

Tilki, Umut
Erkmen, İsmet
Erkmen, Aydan Müşerref

Metadata

Show full item record

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

Item Usage Stats

158
views

0
downloads

Imitation learning is one of the forms of social learning that enables the human or robot agents to learn new skills. The knowledge acquired for imitation can be basically represented as action mapping based on "organ matching" which determines the correspondence between imitator and imitatee, if the imitator and the demonstrator share the same embodiment. In this paper; we aim at imitation of two system with totally different dynamics, imitating each other, where any correspondence is missing. Towards this aim, we adopt a case where the imitator is a fluidic system which dynamics is totally different than the imitatee, that is a human performing different body poses. Our work proposes the fluidics formation control of fluid particles where the formation results from the imitation of observed human body poses. Fluidic formation control layer is responsible of assigning the correct fluid parameters to the swarm formation layer according to the body poses adopted by the human performer: The movement of the fluid particles is modeled using the Smoothed Particle Hydrodynamics (SPH) which is a particle based Lagrangian method for simulation of fluid flows. The region based controller first extract the human body parts generating the regions where the attention is attracted by the imitatee and fits an appropriate ellipses to delimite boundaries of those regions. The ellipse parameters such as center of the ellipses, eccentricity, length of the major and minor axis etc. are used by the fludic layer in order to generate human body poses. This paper introduces our technique and demonstrates the imitation performance of our system.

Subject Keywords

Task

URI

https://hdl.handle.net/11511/54709

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Camera motion blur and its effect on feature detectors Üzer, Ferit; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2010) Perception, hence the usage of visual sensors is indispensable in mobile and autonomous robotics. Visual sensors such as cameras, rigidly mounted on a robot frame are the most common usage scenario. In this case, the motion of the camera due to the motion of the moving platform as well as the resulting shocks or vibrations causes a number of distortions on video frame sequences. Two most important ones are the frame-to-frame changes of the line-of-sight (LOS) and the presence of motion blur in individual fr...
Parametrization of Robotic Deburring Process with Motor Skills from Motion Primitives of Human Skill Model Parvizi, Payam; Ugurlu, Musab Cagri; Açıkgöz, Kemal; Konukseven, Erhan İlhan (2017-08-31) 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 p...
Implementation of a closed-loop action generation system on a humanoid robot through learning by demonstration Tunaoğlu, Doruk; Şahin, Erol; Department of Computer Engineering (2010) In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstr...
Software development for man-machine interface for an industrial robot Cengiz, Mahir Cihan; Kaftanoğlu, Bilgin; Department of Mechanical Engineering (2003) In this study, a robotic software, which controls the robot, is developed. The robot considered is a six degree of freedom robot and it is designed and manufactured in METU. User can send the robot anywhere in space within its workspace, in any orientation. Forward and inverse kinamatics can be executed according to the needs. Simulation framework is embedded into the software for the 3D visualisation of the robot. Any movements can be simulated on the screen. Software also generates the path for the given ...
Online mining of human deep intention by proactive environment changes using deep neural networks Er, Nur Baki; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2015) This thesis focuses on surfacing human deep intention, which is known or assumed, in a smart environment that consists of autonomous robotic systems which can interact with the human. Deep intentions are defined as kind of actions that humans would like to behave but pushed deeper in the stack of the intentions in a daily life. The purpose of the designed system is to observe the human in the smart room for a while and to analyze human’s behaviors to offer the optimal set of system behavior to surface a des...

Citation Formats

U. Tilki, İ. Erkmen, and A. M. Erkmen, “IMITATION OF HUMAN BODY POSES BY THE FORMATION CONTROL OF A FLUIDIC SWARM,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54709.