David Galdeano. LIRMM-UM2, Montpellier, France. Members of CST: Philippe Fraisse, Ahmed Chemori, Sébatien Krut and André Crosnier
|
|
- Aubrey Little
- 6 years ago
- Views:
Transcription
1 David Galdeano LIRMM-UM2, Montpellier, France Members of CST: Philippe Fraisse, Ahmed Chemori, Sébatien Krut and André Crosnier Montpellier, Thursday September 27, 2012
2 Outline of the presentation Context and motivations Used tools Motion capture system (VICON) Developed simulator Proposed control scheme Simulation results Conclusion Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO
3 Context and motivations Humanoid walking [Kajita, IROS 2010] Dynamically stable Constructed using various phases Discontinuous How to produce natural motion for humanoid robot? Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO
4 Context and motivations Human walking Dynamically stable Energetically efficient No phases Smooth Continuous Human-like walking is an obvious goal for humanoid robotics Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO
5 A typical control architecture in humanoid robotics Pattern generator Walking Parameters Pattern generator feet/zmp positions Robot Model SHERPA robot q d Controller Articulations control U Robot SHERPA robot Contact forces control q, qp Feet position modification Contact forces measurement Posture Foot landing control Position error of the body Balance control Posture control Stabilizer 05/07/2012 D. Galdeano IFAC SYROCO
6 Context and motivations Human motions Simulated model or robot Control scheme Objective : Using of human motion data to improve the humanoid dynamic stability and energy consumption during walking Montpellier, Thursday September 27, 2012 D. Galdeano IFAC CST 2012 SYROCO
7 State of art Whole body motion control Task-based schemes Human-Data based schemes Class 1: Online computation Class 2: Offline computation Objective function [Liegeois, 1977] Task priority based redundancy control [Siciliano et al.,1991, Nakamura et al., 1987] Stack of task [Mansard., 2007] Balance/Tracking controller [Yamane et al., ] Imitation [Shaal et al., , Calderon & Hu 2005] Motion Primitives [Nakaoka et al., ] Gait parameter extraction [Harada et al.,2009] Scale and optimization [Suleiman et al., 2008] Human Normalized model [Montecillo et al.,2010] 7
8 Human data based whole body motion control using offline calculation [Nakaoka et al., 2003, 2005] Data from human motion capture are used as motion primitives to produce postural imitation (only postural motions, no walking). [Harada et al., 2009] Data from human motion capture are used to find gait s parameters. [Suleiman et al., 2008] Data from human motion capture are first scaled to humanoid joint position, then an optimization with constraint is used. Pros: Offline computations allows optimized motions Cons: Offline computation do not allow reactive motions 8
9 Human data based whole body motion control using online calculation [Schaal, 1999 ; Schaal et al., 2003 ; Calderon & Hu, 2005] Data from human motion capture are used to feed a learning system to produce accurate movement primitives. [Yamane & Hodgins, 2009 ; Yamane et al., 2010] Two controllers are used in this application. First controller : a balance controller. Second controller : joint space trajectory tracking Pros: Reactive motions using feedback from sensors Cons: No walking motions are reproduced 9
10 Tasks based whole body motion control [Liegeois, 1977, Siciliano et al.,1991, Nakamura et al., 1987, Mansard., 2007] Task formalism is used to distribute the motion on every degrees of freedom. Pros: Efficient to achieve several objectives Cons: Not based on human motion [Montecillo-Puente et al., 2010] Data from human motion capture are performed in real time to produce postural imitation (postural motion, no walking). 10
11 Motivations Reference motion : from human motion capture Differences: Flexible/Rigid Different DoF Different Power Contacts Similarities: CoM Feet cycle Reduced set of human data: Relative feet position (3) + CoM (3) Articular trajectories (22) 11
12 Motivations Basic idea: Task-priority formalism [Nakamura, 1987] How : Two tasks - Relative feet position tracking. - CoM trajectory tracking. Advantages: Continuous control framework: No decomposition into distinct phases, one control law. 12
13 Motion Capture system Context: Walking motion analysis project LABLAB, University of Rome Foro Italico, Pr. Capozzo, Department of Human Movement and Sports Sciences. Equipment : 1 host PC 10 Vicon cameras 3 Forces plates 13
14 Motion Capture system Study: 15 Subjects Different walking speed 35 markers using Plug-in Gait template Reconstruction of movement using Vicon Nexus Estimation of CoM using Lifemod 14
15 Developed simulator ZMP articulations Contact forces with ground Includes whole-body models of different robots. Kinematic and Dynamic model. Contact model (Multi-contacts). Link in tree representation allows easy modification. Modular approach allows to switch between different models. Written in C. OpenGL for graphics. GSL for matrix calculations. 15
16 Developed simulator Newton-Euler dynamic formulation : Avec: allow an recursive computation of rigid multi body system dynamic. Then, dynamic is expressed on canonical form: With: : Mass matrix. : Applied torques. : Coriolis centrifugal matrix. : Contact forces. : Gravity terms. 16
17 Developed simulator Contact model: Contact are calculated as spring mass damper system as describe in [Rodas, 2010] with 4 or more contacts point by foot: A saturation function a prevents a negative contact force ( lift off. ) on Foot Ground 17
18 Prioritized tasks What is a task? Jacobian of relative feet position tracking task is defined as: Articular position error: With null-space projection: 18
19 Basic idea of the proposed control scheme Motion Capture Simulated model Control scheme 19
20 First task Block diagram of the proposed control scheme Second task Null space projection 20
21 Mathematical formulation of the proposed control scheme Jacobian of relative feet position tracking task is defined as: Articular position error: Jacobian of CoM tracking task defined as: Final formulation: With: 21
22 Simulation results Simulation of the proposed control scheme: Data form human motion capture without adaptation 22
23 Simulation results Simulation of the proposed control scheme: Data form human motion capture with scaling of the CoM trajectories 23
24 Simulation results Simulation of the proposed control scheme: Hip trajectories 24
25 Simulation results Simulation of the proposed control scheme: Left leg articular trajectories in the sagittal plane 25
26 Simulation results Simulation with Dynamic: No feet movement CoM moves up and down 26
27 Experimentation results First experimental results using HOAP3 robot: No feet movement CoM moves up and down Need more verifications before experiments on the ground Scaling issues with walking motions 27
28 Conclusion In cinematic simulation: Whole body motion based on a reduced set of human data No distinct phases decomposition Continuous control framework Human-like walking without imitation In Dynamic simulation: Whole body motion based on sinusoid functions Use of tasks framework Issues with walking motions (stability) In Experimentation: Whole body motion based on sinusoid functions Use of tasks framework Issues with walking motions (Scaling) 28
29 Short term perspectives Design a general scaling procedure. Design of a controller for dynamic simulations. ZMP in the control scheme. Real-time implementation of walking motion on a humanoid robot : HOAP3. Long term perspectives Stability and robustness analysis. Energetic consumption analysis. Propose some complementary tasks to improve the autonomy. Study of arms motions effects on energy consumption. Implementation in other robot architecture: SHERPA, HRP4. 29
30 Publications Accepted paper: David Galdeano, Vincent Bonnet, Moussâb Bennehar, Philippe Fraisse and Ahmed Chemori, Partial Human Data in Design of Human-Like Walking Control in Humanoid Robotics. 10th international IFAC symposium on robot Control (SYROCO2012), Dubrovnik, Croatia, September 05-07, Presentations: D. Galdeano, A. Chemori, S. Krut, Optimal Pattern Generator Based on a Three-Mass Linear Inverted Pendulum Model for Dynamic Walking, Workshop on Humanoid and Legged Robots, HLR 2011, Paris, France, February 14-15,
31 RÉFÉRENCES [Calderon & Hu 2005] Calderon, C., & Hu, H. (2005). Robot imitation from human body movements. In In proceeding third international symposium on imitation in animals and artifacts (aisb 05). [Galdeano et al.,2012] Galdeano, D., Bonnet, V., Bennehar, M., Fraisse, P., & Chemori, A., (2012). Partial Human Data in Design of Human-Like Walking Control in Humanoid Robotics. 10th international IFAC symposium on robot Control (SYROCO2012), September 05-07, [Harada et al.,2009] Harada, K., Miura, K., Morisawa, M., Kaneko, K., Nakaoka, S., Kanehiro, F., et al. (2009). Toward human-like walking pattern generator. In Proceedings of the 2009 ieee/rsj international conference on intelligent robots and systems (iros 09) (pp ). Albuquerque, NM, USA. [Montecillo et al.,2010] Montecillo-Puente, F., Sreenivasa, M., & Laumond, J. (2010). On realtime whole-body human to humanoid motion transfer. In International conference on informatics in control, automation and robotics (icinco
32 RÉFÉRENCES [Nakaoka et al., 2005] Nakaoka, S., Nakazawa, A., Kanehiro, F., Kaneko, K., Morisawa, M., & Ikeuchi, K. (2005). Task model of lower body motion for a biped humanoid robot to imitate human dances. In Proceedings ofinternational conference on intelligent robots and systems (iros 05) (pp ). [Nakaoka et al., 2003] Nakaoka, S., Nakazawa, A., Yokoi, K., Hirukawa, H., & Ikeuchi, K. (2003). Generating whole body motions for a biped humanoid robot from captured human dances. In Proceedings of international conference on robotics and automation (icra 03) (Vol. 3, pp ). [Rodas, 2010] RODAS, C. R. (2010). Contributions à la commande d un robot bipède 3d. IRCCYN, Université de Nantes : PhD thesis. [Schaal, 1999] Schaal, S. (1999). Is imitation learning the route to humanoid robots. Trends in cognitive sciences, 3(6), [Schaal et al., 2003] Schaal, S., Ijspeert, A., & Billard, A. (2003). Computational approaches to motor learning by imitation. Philosophical Transactions of the Royal Society of London. Series B : Biological Sciences, 358(1431),
33 RÉFÉRENCES [Suleiman et al., 2008] Suleiman, W., Yoshida, E., Kanehiro, F., Laumond, J., & Monin, A. (2008). On human motion imitation by humanoid robot. In Proceedings of international conference on robotics and automation (icra 08) (pp ). [Takanishiet al., 1989] Takanishi, A., Tochizawa, M., Karaki, H., & Kato, I. (1989). Dynamic biped walking stabilized with optimal trunk and waist motion. In Proceedings of international workshop on intelligent robots and systems : The autonomous mobile robots and its applications (iros 89) (pp ). [Yamaguchi et al.,1993] Yamaguchi, J., Takanishi, A., & Kato, I. (1993). Development of a biped walking robot compensating for three-axis moment by trunk motion. In Proceedings of international conference on intelligent robots and systems (iros 93) (Vol. 1, pp ). [Yamane et al.,2010] Yamane, K., Anderson, S., & Hodgins, J. (2010). Controlling humanoid robots with human motion data : Experimental validation. In Proceedings of international conference on humanoid robots (humanoids 10) (p ). 33
34 [Yamane et al.,2009] Yamane, K., & Hodgins, J. (2009). Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. In Proceedings of international conference on intelligent robots and systems (iros 09) (pp ). [Mansard et al.,2007] Mansard, N., & Chaumette, F. (2007) Task Sequencing for High Level Sensor-Based Control. IEEE Transactions on Robotics. [De Lasa et al.,2010] Mordatch, I., De Lasa, M., & Hertzmann, A. (2010) Robust Physics-Based Locomotion Using Low-Dimensional Planning. ACM Transactions on Graphics, (Proc. SIGGRAPH). 34
Controlling Humanoid Robots with Human Motion Data: Experimental Validation
21 IEEE-RAS International Conference on Humanoid Robots Nashville, TN, USA, December 6-8, 21 Controlling Humanoid Robots with Human Motion Data: Experimental Validation Katsu Yamane, Stuart O. Anderson,
More informationA sliding walk method for humanoid robots using ZMP feedback control
A sliding walk method for humanoid robots using MP feedback control Satoki Tsuichihara, Masanao Koeda, Seiji Sugiyama, and Tsuneo oshikawa Abstract In this paper, we propose two methods for a highly stable
More informationMotion Retargeting for Humanoid Robots Based on Identification to Preserve and Reproduce Human Motion Features
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Congress Center Hamburg Sept 28 - Oct 2, 2015. Hamburg, Germany Motion Retargeting for Humanoid Robots Based on Identification
More informationRobust Control of Bipedal Humanoid (TPinokio)
Available online at www.sciencedirect.com Procedia Engineering 41 (2012 ) 643 649 International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012) Robust Control of Bipedal Humanoid (TPinokio)
More informationInteraction Mesh Based Motion Adaptation for Biped Humanoid Robots
Interaction Mesh Based Motion Adaptation for Biped Humanoid Robots Shin ichiro Nakaoka 12 and Taku Komura 1 Abstract Adapting human motion data for humanoid robots can be an efficient way to let them conduct
More informationHuman Motion Tracking Control with Strict Contact Force Constraints for Floating-Base Humanoid Robots
2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids). October 15-17, 2013. Atlanta, GA Human Motion Tracking Control with Strict Contact Force Constraints for Floating-Base Humanoid
More informationMotion Planning for Whole Body Tasks by Humanoid Robots
Proceedings of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Canada July 5 Motion Planning for Whole Body Tasks by Humanoid Robots Eiichi Yoshida 1, Yisheng Guan 1, Neo
More informationSimultaneous Tracking and Balancing of Humanoid Robots for Imitating Human Motion Capture Data
The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems October 11-15, 2009 St. Louis, USA Simultaneous Tracking and Balancing of Humanoid Robots for Imitating Human Motion Capture
More informationSimplified Walking: A New Way to Generate Flexible Biped Patterns
1 Simplified Walking: A New Way to Generate Flexible Biped Patterns Jinsu Liu 1, Xiaoping Chen 1 and Manuela Veloso 2 1 Computer Science Department, University of Science and Technology of China, Hefei,
More informationNao Devils Dortmund. Team Description Paper for RoboCup Matthias Hofmann, Ingmar Schwarz, and Oliver Urbann
Nao Devils Dortmund Team Description Paper for RoboCup 2017 Matthias Hofmann, Ingmar Schwarz, and Oliver Urbann Robotics Research Institute Section Information Technology TU Dortmund University 44221 Dortmund,
More informationIntuitive and Flexible User Interface for Creating Whole Body Motions of Biped Humanoid Robots
The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems October 18-22, 2010, Taipei, Taiwan Intuitive and Flexible User Interface for Creating Whole Body Motions of Biped Humanoid
More informationUpper Body Joints Control for the Quasi static Stabilization of a Small-Size Humanoid Robot
Upper Body Joints Control for the Quasi static Stabilization of a Small-Size Humanoid Robot Andrea Manni, Angelo di Noi and Giovanni Indiveri Dipartimento Ingegneria dell Innovazione, Università di Lecce
More informationFast foot prints re-planning and motion generation during walking in physical human-humanoid interaction
Fast foot prints re-planning and motion generation during walking in physical human-humanoid interaction Olivier Stasse, Paul Evrard, Nicolas Perrin, Nicolas Mansard, Abderrahmane Kheddar Abstract In this
More informationRetrieving Contact Points Without Environment Knowledge
2012 12th IEEE-RAS International Conference on Humanoid Robots Nov.29-Dec.1, 2012. Business Innovation Center Osaka, Japan Retrieving Contact Points Without Environment Knowledge Sébastien Lengagne, Ömer
More informationAdaptive Motion Control: Dynamic Kick for a Humanoid Robot
Adaptive Motion Control: Dynamic Kick for a Humanoid Robot Yuan Xu and Heinrich Mellmann Institut für Informatik, LFG Künstliche Intelligenz Humboldt-Universität zu Berlin, Germany {xu,mellmann}@informatik.hu-berlin.de
More informationControl Approaches for Walking and Running
DLR.de Chart 1 > Humanoids 2015 > Christian Ott > 02.11.2015 Control Approaches for Walking and Running Christian Ott, Johannes Englsberger German Aerospace Center (DLR) DLR.de Chart 2 > Humanoids 2015
More informationGenerating Whole Body Motions for a Biped Humanoid Robot from Captured Human Dances
Generating Whole Body Motions for a Biped Humanoid Robot from Captured Human Dances Shinichiro Nakaoka Atsushi Nakazawa Kazuhito Yokoi Hirohisa Hirukawa Katsushi Ikeuchi Institute of Industrial Science,
More informationUsing the Generalized Inverted Pendulum to generate less energy-consuming trajectories for humanoid walking
Using the Generalized Inverted Pendulum to generate less energy-consuming trajectories for humanoid walking Sahab Omran, Sophie Sakka, Yannick Aoustin To cite this version: Sahab Omran, Sophie Sakka, Yannick
More informationEvolutionary Motion Design for Humanoid Robots
Evolutionary Motion Design for Humanoid Robots Toshihiko Yanase Department of Frontier Informatics The University of Tokyo Chiba 277-8561, Japan yanase@iba.k.u-tokyo.ac.jp Hitoshi Iba Department of Frontier
More informationLeg Motion Primitives for a Humanoid Robot to Imitate Human Dances
Leg Motion Primitives for a Humanoid Robot to Imitate Human Dances Shinichiro Nakaoka 1, Atsushi Nakazawa 2, Kazuhito Yokoi 3 and Katsushi Ikeuchi 1 1 The University of Tokyo,Tokyo, Japan (nakaoka@cvl.iis.u-tokyo.ac.jp)
More informationMotion Planning of Emergency Stop for Humanoid Robot by State Space Approach
Motion Planning of Emergency Stop for Humanoid Robot by State Space Approach Mitsuharu Morisawa, Kenji Kaneko, Fumio Kanehiro, Shuuji Kajita, Kiyoshi Fujiwara, Kensuke Harada, Hirohisa Hirukawa National
More informationModelling and simulation of the humanoid robot HOAP-3 in the OpenHRP3 platform
Modelling and simulation of the humanoid robot -3 in the 3 platform C.A. Monje, P. Pierro, T. Ramos, M. González-Fierro, C. Balaguer. Abstract The aim of this work is to model and simulate the humanoid
More informationFalling forward of humanoid robot based on similarity with parametric optimum
Acta Technica 62 (2017), No. 5A, 201212 c 2017 Institute of Thermomechanics CAS, v.v.i. Falling forward of humanoid robot based on similarity with parametric optimum Xiaokun Leng 1, Songhao Piao 2, Lin
More informationHumanoid Full-Body Manipulation Motion Planning. with Multiple Initial-Guess and Key Postures
Humanoid Full-Body Manipulation Motion Planning with Multiple Initial-Guess and Key Postures Ph.D Research Qualification Report Department of Mechanical Engineering Bowei Tang September 15, 2014 Bowei
More informationSynthesis of Controllers for Stylized Planar Bipedal Walking
Synthesis of Controllers for Stylized Planar Bipedal Walking Dana Sharon, and Michiel van de Panne Department of Computer Science University of British Columbia Vancouver, BC, V6T 1Z4, Canada {dsharon,van}@cs.ubc.ca
More informationSit-to-Stand Task on a Humanoid Robot from Human Demonstration
2010 IEEE-RAS International Conference on Humanoid Robots Nashville, TN, USA, December 6-8, 2010 Sit-to-Stand Task on a Humanoid Robot from Human Demonstration Michael Mistry, Akihiko Murai, Katsu Yamane,
More informationLeg Motion Primitives for a Dancing Humanoid Robot
Leg Motion Primitives for a Dancing Humanoid Robot Shinichiro Nakaoka, Atsushi Nakazawa, Kazuhito Yokoi and Katsushi Ikeuchi Institute of Industrial Science, The University of Tokyo 4-6-1 Komaba, Meguro-ku,
More informationReal-time Replanning Using 3D Environment for Humanoid Robot
Real-time Replanning Using 3D Environment for Humanoid Robot Léo Baudouin, Nicolas Perrin, Thomas Moulard, Florent Lamiraux LAAS-CNRS, Université de Toulouse 7, avenue du Colonel Roche 31077 Toulouse cedex
More informationResearch Subject. Dynamics Computation and Behavior Capture of Human Figures (Nakamura Group)
Research Subject Dynamics Computation and Behavior Capture of Human Figures (Nakamura Group) (1) Goal and summary Introduction Humanoid has less actuators than its movable degrees of freedom (DOF) which
More informationOnline Gain Switching Algorithm for Joint Position Control of a Hydraulic Humanoid Robot
Online Gain Switching Algorithm for Joint Position Control of a Hydraulic Humanoid Robot Jung-Yup Kim *, Christopher G. Atkeson *, Jessica K. Hodgins *, Darrin C. Bentivegna *,** and Sung Ju Cho * * Robotics
More informationIntegrating Dynamics into Motion Planning for Humanoid Robots
Integrating Dynamics into Motion Planning for Humanoid Robots Fumio Kanehiro, Wael Suleiman, Florent Lamiraux, Eiichi Yoshida and Jean-Paul Laumond Abstract This paper proposes an whole body motion planning
More informationTable of Contents. Chapter 1. Modeling and Identification of Serial Robots... 1 Wisama KHALIL and Etienne DOMBRE
Chapter 1. Modeling and Identification of Serial Robots.... 1 Wisama KHALIL and Etienne DOMBRE 1.1. Introduction... 1 1.2. Geometric modeling... 2 1.2.1. Geometric description... 2 1.2.2. Direct geometric
More informationFast and Dynamically Stable Optimization-based Planning for High-DOF Human-like Robots
Fast and Dynamically Stable Optimization-based Planning for High-DOF Human-like Robots Chonhyon Park and Dinesh Manocha http://gamma.cs.unc.edu/itomp/ (Videos included) Abstract We present a novel optimization-based
More informationDynamic Balancing and Walking for Real-time 3D Characters
Dynamic Balancing and Walking for Real-time 3D Characters Ben Kenwright, Richard Davison, Graham Morgan School of Computing Science, Newcastle University, UK {b.kenwright, richard.davison4, graham.morgan}@ncl.ac.uk
More informationA Walking Pattern Generator for Biped Robots on Uneven Terrains
A Walking Pattern Generator for Biped Robots on Uneven Terrains Yu Zheng, Ming C. Lin, Dinesh Manocha Albertus Hendrawan Adiwahono, Chee-Meng Chew Abstract We present a new method to generate biped walking
More informationSmall-Space Controllability of a Walking Humanoid Robot
Small-Space Controllability of a Walking Humanoid Robot Se bastien Dalibard, Antonio El Khoury, Florent Lamiraux, Michel Taı x, Jean-Paul Laumond hal-00602384, version 2-19 Sep 2011 CNRS ; LAAS ; 7 avenue
More informationOnline Generation of Humanoid Walking Motion based on a Fast. The University of Tokyo, Tokyo, Japan,
Online Generation of Humanoid Walking Motion based on a Fast Generation Method of Motion Pattern that Follows Desired ZMP Koichi Nishiwaki 1, Satoshi Kagami 2,Yasuo Kuniyoshi 1, Masayuki Inaba 1,Hirochika
More informationReal-time full body motion imitation on the COMAN humanoid robot Andrej Gams, Jesse van den Kieboom, Florin Dzeladini, Aleš Ude and Auke Jan Ijspeert
Robotica (2015) volume 33, pp. 1049 1061. Cambridge University Press 2014 doi:10.1017/s0263574714001477 Real-time full body motion imitation on the COMAN humanoid robot Andrej Gams, Jesse van den Kieboom,
More informationJožef Stefan Institute. Jamova 39, 1000 Ljubljana, Slovenia. École Polytechnique Fédérale de Lausanne. Station 14, CH-1015 Lausanne, Switzerland
1 2 REAL-TIME FULL BODY MOTION IMITATION ON THE COMAN HUMANOID ROBOT 3 Andrej Gams 1,2, Jesse van den Kieboom 2, Florin Dzeladini 2, Aleš Ude 1 and Auke Jan Ijspeert 2 1 Dept. of Automation, Biocybernetics
More informationMotion Control of Wearable Walking Support System with Accelerometer Considering Swing Phase Support
Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, Technische Universität München, Munich, Germany, August 1-3, Motion Control of Wearable Walking Support
More informationHUMANOID robots have recently attracted growing social
IEEE TRANSACTIONS ON ROBOTICS 1 Motion Retargeting for Humanoid Robots Based on Simultaneous Morphing Parameter Identification and Motion Optimization Ko Ayusawa, Member, IEEE, and Eiichi Yoshida, Senior
More informationStudy on Dynamics Identification of the Foot Viscoelasticity of a Humanoid Robot
Preprints of the 19th World Congress The International Federation of Automatic Control Cape Town, South Africa. August 24-29, 214 Study on Dynamics Identification of the Foot Viscoelasticity of a Humanoid
More informationMotion Recognition and Generation for Humanoid based on Visual-Somatic Field Mapping
Motion Recognition and Generation for Humanoid based on Visual-Somatic Field Mapping 1 Masaki Ogino, 1 Shigeo Matsuyama, 1 Jun ichiro Ooga, and 1, Minoru Asada 1 Dept. of Adaptive Machine Systems, HANDAI
More informationMotion Planning for Humanoid Robots: Highlights with HRP-2
Motion Planning for Humanoid Robots: Highlights with HRP-2 Eiichi Yoshida 1,2 and Jean-Paul Laumond 2 AIST/IS-CNRS/ST2I Joint French-Japanese Robotics Laboratory (JRL) 1 Intelligent Systems Research Institute,
More informationInverse Kinematics for Humanoid Robots using Artificial Neural Networks
Inverse Kinematics for Humanoid Robots using Artificial Neural Networks Javier de Lope, Rafaela González-Careaga, Telmo Zarraonandia, and Darío Maravall Department of Artificial Intelligence Faculty of
More informationModeling Physically Simulated Characters with Motion Networks
In Proceedings of Motion In Games (MIG), Rennes, France, 2012 Modeling Physically Simulated Characters with Motion Networks Robert Backman and Marcelo Kallmann University of California Merced Abstract.
More informationSHORT PAPER Efficient reaching motion planning method for low-level autonomy of teleoperated humanoid robots
Advanced Robotics, 214 Vol. 28, No. 7, 433 439, http://dx.doi.org/1.18/1691864.213.876931 SHORT PAPER Efficient reaching motion planning method for low-level autonomy of teleoperated humanoid robots Fumio
More informationSound and fast footstep planning for humanoid robots
Sound and fast footstep planning for humanoid robots Nicolas Perrin, Olivier Stasse, Léo Baudouin, Florent Lamiraux, Eiichi Yoshida Abstract In this paper we present some concepts for sound and fast footstep
More informationHuman Motion Reconstruction by Direct Control of Marker Trajectories
Human Motion Reconstruction by Direct Control of Marker Trajectories Emel Demircan, Luis Sentis, Vincent De Sapio and Oussama Khatib Artificial Intelligence Laboratory, Stanford University, Stanford, CA
More informationHumanoid Walking Control using the Capture Point
Humanoid Walking Control using the Capture Point Christian Ott and Johannes Englsberger Institute of Robotis and Mehatronis German Aerospae Center (DLR e.v.) hristian.ott@dlr.de Joint torque sensing &
More informationMobile Robots Locomotion
Mobile Robots Locomotion Institute for Software Technology 1 Course Outline 1. Introduction to Mobile Robots 2. Locomotion 3. Sensors 4. Localization 5. Environment Modelling 6. Reactive Navigation 2 Today
More informationModeling of Humanoid Systems Using Deductive Approach
INFOTEH-JAHORINA Vol. 12, March 2013. Modeling of Humanoid Systems Using Deductive Approach Miloš D Jovanović Robotics laboratory Mihailo Pupin Institute Belgrade, Serbia milos.jovanovic@pupin.rs Veljko
More informationBalanced Walking with Capture Steps
Balanced Walking with Capture Steps Marcell Missura and Sven Behnke Autonomous Intelligent Systems, Computer Science, Univ. of Bonn, Germany {missura,behnke}@cs.uni-bonn.de http://ais.uni-bonn.de Abstract.
More informationUsing Eigenposes for Lossless Periodic Human Motion Imitation
The 9 IEEE/RSJ International Conference on Intelligent Robots and Systems October -5, 9 St. Louis, USA Using Eigenposes for Lossless Periodic Human Motion Imitation Rawichote Chalodhorn and Rajesh P. N.
More informationInverse Kinematics for Humanoid Robots Using Artificial Neural Networks
Inverse Kinematics for Humanoid Robots Using Artificial Neural Networks Javier de Lope, Rafaela González-Careaga, Telmo Zarraonandia, and Darío Maravall Department of Artificial Intelligence Faculty of
More informationLOCOMOTION AND BALANCE CONTROL OF HUMANOID ROBOTS WITH DYNAMIC AND KINEMATIC CONSTRAINTS. Yu Zheng
LOCOMOTION AND BALANCE CONTROL OF HUMANOID ROBOTS WITH DYNAMIC AND KINEMATIC CONSTRAINTS Yu Zheng A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment
More informationMotion Planning of Human-Like Robots using Constrained Coordination
Motion Planning of Human-Like Robots using Constrained Coordination Liangjun Zhang Jia Pan Dinesh Manocha {zlj,panj,dm}@cs.unc.edu Dept. of Computer Science, University of North Carolina at Chapel Hill
More informationA NOUVELLE MOTION STATE-FEEDBACK CONTROL SCHEME FOR RIGID ROBOTIC MANIPULATORS
A NOUVELLE MOTION STATE-FEEDBACK CONTROL SCHEME FOR RIGID ROBOTIC MANIPULATORS Ahmad Manasra, 135037@ppu.edu.ps Department of Mechanical Engineering, Palestine Polytechnic University, Hebron, Palestine
More informationDevelopment of Vision System on Humanoid Robot HRP-2
Development of Vision System on Humanoid Robot HRP-2 Yutaro Fukase Institute of Technology, Shimizu Corporation, Japan fukase@shimz.co.jp Junichiro Maeda Institute of Technology, Shimizu Corporation, Japan
More informationHuman motion control with physically plausible foot contact models
Vis Comput (2015) 31:883 891 DOI 10.1007/s00371-015-1097-8 ORIGINAL ARTICLE Human motion control with physically plausible foot contact models Jongmin Kim 1 Hwangpil Park 2 Jehee Lee 2 Taesoo Kwon 1 Published
More informationAutonomous and Mobile Robotics Prof. Giuseppe Oriolo. Humanoid Robots 2: Dynamic Modeling
Autonomous and Mobile Robotics rof. Giuseppe Oriolo Humanoid Robots 2: Dynamic Modeling modeling multi-body free floating complete model m j I j R j ω j f c j O z y x p ZM conceptual models for walking/balancing
More informationInverse Dynamics Control of Floating Base Systems Using Orthogonal Decomposition
2 IEEE International Conference on Robotics and Automation Anchorage Convention District May 3-8, 2, Anchorage, Alaska, USA Inverse Dynamics Control of Floating Base Systems Using Orthogonal Decomposition
More informationSimulation. x i. x i+1. degrees of freedom equations of motion. Newtonian laws gravity. ground contact forces
Dynamic Controllers Simulation x i Newtonian laws gravity ground contact forces x i+1. x degrees of freedom equations of motion Simulation + Control x i Newtonian laws gravity ground contact forces internal
More informationA CONTROL ARCHITECTURE FOR DYNAMICALLY STABLE GAITS OF SMALL SIZE HUMANOID ROBOTS. Andrea Manni,1, Angelo di Noi and Giovanni Indiveri
A CONTROL ARCHITECTURE FOR DYNAMICALLY STABLE GAITS OF SMALL SIZE HUMANOID ROBOTS Andrea Manni,, Angelo di Noi and Giovanni Indiveri Dipartimento di Ingegneria dell Innovazione, Università di Lecce, via
More informationOpen Access The Kinematics Analysis and Configuration Optimize of Quadruped Robot. Jinrong Zhang *, Chenxi Wang and Jianhua Zhang
Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 014, 6, 1685-1690 1685 Open Access The Kinematics Analysis and Configuration Optimize of Quadruped
More informationNeural Network model for a biped robot
Neural Network model for a biped robot Daniel Zaldívar 1,2, Erik Cuevas 1,2, Raúl Rojas 1. 1 Freie Universität Berlin, Institüt für Informatik, Takustr. 9, D-14195 Berlin, Germany {zaldivar, cuevas, rojas}@inf.fu-berlin.de
More informationHumanoid Robotics Modeling by Dynamic Fuzzy Neural Network
Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 1-17, 7 umanoid Robotics Modeling by Dynamic Fuzzy Neural Network Zhe Tang, Meng Joo Er, and Geok See Ng
More informationLearning to Walk through Imitation
Abstract Programming a humanoid robot to walk is a challenging problem in robotics. Traditional approaches rely heavily on prior knowledge of the robot's physical parameters to devise sophisticated control
More informationExperimental Evaluation of the Dynamic Simulation of Biped Walking of Humanoid Robots
Proceedings of the 2003 IEEE International Conference on Robotics & Automation Taipei, Taiwan, September 14-19, 2003 Experimental Evaluation of the Dynamic Simulation of Biped Walking of Humanoid Robots
More informationDevelopment of an optomechanical measurement system for dynamic stability analysis
Development of an optomechanical measurement system for dynamic stability analysis Simone Pasinetti Dept. of Information Engineering (DII) University of Brescia Brescia, Italy simone.pasinetti@unibs.it
More informationPublished in: Proceedings of RoboCup 2012, Symposium Papers and Team Description Papers, June 2012, Mexico City, Mexico
Tech United Eindhoven obocup adult size humanoid team description 212 van Zutven, P.W.M.; van Dalen, S.J.; Assman, T.M.; Caarls, J.; Çilli, C.; Aarts, M.A.P.; Boshoven, T.P.M.; Mironchyk, P.; Ilhan, E.;
More informationMotion Planning for a Vigilant Humanoid Robot
9th IEEE-RAS International Conference on Humanoid Robots December 7-10, 2009 Paris, France Motion Planning for a Vigilant Humanoid Robot Jean-Bernard Hayet 1 Claudia Esteves 2 Gustavo Arechavaleta 3 Eiichi
More informationBiped Walking Control Based on Hybrid Position/Force Control
The 29 IEEE/RSJ International Conference on Intelligent Robots and Systems October -5, 29 St. Louis, USA Biped Walking Control Based on Hybrid Position/Force Control Thomas Buschmann, Sebastian Lohmeier
More informationUSING THE GENERALIZED INVERTED PENDULUM TO GENERATE LESS ENERGY-CONSUMING TRAJECTORIES FOR HUMANOID WALKING
A R C H I V E O F M E C H A N I C A L E N G I N E E R I N G VOL. LXIII 2016 Number 2 10.1515/meceng-2016-0014 Key words: Inverted pendulum, pivot point, walking robots, energy consumption, joint torques
More informationSerially-Linked Parallel Leg Design for Biped Robots
December 13-15, 24 Palmerston North, New ealand Serially-Linked Parallel Leg Design for Biped Robots hung Kwon, Jung H. oon, Je S. eon, and Jong H. Park Dept. of Precision Mechanical Engineering, School
More informationAn Efficient Method for Composing Whole Body Motions of a Humanoid Robot
An Efficient Method for Composing Whole Body Motions of a Humanoid Robot Shinichiro NAKAOKA Atsushi NAKAZAWA Katsushi IKEUCHI Institute of Industrial Science, The University of Tokyo {nakaoka, ki}@cvl.iis.u-tokyo.ac.jp
More informationBiped Walking Pattern Generation by using Preview Control of Zero-Moment Point
Proceedings of the 23 IEEE International Conference on Robotics & Automation Taipei, Taiwan, September 4-9, 23 Biped Walking Pattern Generation by using Preview Control of Zero-Moment Point Shuuji KAJITA,
More informationWhole Body Humanoid Control From Human Motion Descriptors
2008 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 2008 Whole Body Humanoid Control From Human Motion Descriptors Behzad Dariush Michael Gienger Bing Jian Christian
More informationMotion autonomy for humanoids: experiments on HRP-2 No. 14. Introduction
COMPUTER ANIMATION AND VIRTUAL WORLDS Comp. Anim. Virtual Worlds 2009; 20: 511 522 Published online 13 February 2009 in Wiley InterScience (www.interscience.wiley.com).280 Motion autonomy for humanoids:
More informationLearning Footstep Prediction from Motion. capture
Learning Footstep Prediction from Motion Capture Andreas Schmitz, Marcell Missura, and Sven Behnke University of Bonn, Computer Science VI, Autonomous Intelligent Systems Roemerstr. 164, 53117 Bonn, Germany
More informationWe are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 3,700 108,500 1.7 M Open access books available International authors and editors Downloads Our
More informationSTUDY OF ZMP MEASUREMENT FOR BIPEDROBOT USING FSR SENSOR
STUDY OF ZMP MEASUREMENT FOR BIPEDROBOT USING FSR SENSOR Bong Gyou Shim, *Eung Hyuk Lee, **Hong Ki Min, Seung Hong Hong Department of Electronic Engineering, Inha University *Department of Electronic Engineering,
More informationLast Time? Animation, Motion Capture, & Inverse Kinematics. Today. Keyframing. Physically-Based Animation. Procedural Animation
Last Time? Animation, Motion Capture, & Inverse Kinematics Navier-Stokes Equations Conservation of Momentum & Mass Incompressible Flow Today How do we animate? Keyframing Procedural Animation Physically-Based
More informationSelf-Collision Detection and Prevention for Humanoid Robots. Talk Overview
Self-Collision Detection and Prevention for Humanoid Robots James Kuffner, Jr. Carnegie Mellon University Koichi Nishiwaki The University of Tokyo Satoshi Kagami Digital Human Lab (AIST) Masayuki Inaba
More informationDesign and Stiffness Analysis of 12 DoF Poppy-inspired Humanoid
Design and Stiffness Analysis of 12 DoF Poppy-inspired Humanoid Dmitry Popov, Alexandr Klimchik and Ilya Afanasyev Institute of Robotics, Innopolis University, Universitetskaya Str. 1, Innopolis, Russia
More informationLast Time? Inverse Kinematics. Today. Keyframing. Physically-Based Animation. Procedural Animation
Last Time? Inverse Kinematics Navier-Stokes Equations Conservation of Momentum & Mass Incompressible Flow Today How do we animate? Keyframing Procedural Animation Physically-Based Animation Forward and
More informationContributions à la commande adaptative non linéaire des robots parallèles
Commité de Suivi de Thèse (CST) Ecole Doctorale : Information, Structures et Systèmes Contributions à la commande adaptative non linéaire des robots parallèles Bennehar Moussab Doctorant en 2 ème année
More informationHomework 2 Questions? Animation, Motion Capture, & Inverse Kinematics. Velocity Interpolation. Handing Free Surface with MAC
Homework 2 Questions? Animation, Motion Capture, & Inverse Kinematics Velocity Interpolation Original image from Foster & Metaxas, 1996 In 2D: For each axis, find the 4 closest face velocity samples: Self-intersecting
More informationWritten exams of Robotics 2
Written exams of Robotics 2 http://www.diag.uniroma1.it/~deluca/rob2_en.html All materials are in English, unless indicated (oldies are in Year Date (mm.dd) Number of exercises Topics 2018 07.11 4 Inertia
More informationStrategies for Humanoid Robots to Dynamically Walk over Large Obstacles
Strategies for Humanoid Robots to Dynamically Walk over Large Obstacles Olivier Stasse, Bjorn Verrelst, Bram Vanderborght, Kazuhito Yokoi To cite this version: Olivier Stasse, Bjorn Verrelst, Bram Vanderborght,
More informationApproximate Policy Transfer applied to Simulated. Bongo Board balance toy
Approximate Policy Transfer applied to Simulated Bongo Board Balance Stuart O. Anderson, Jessica K. Hodgins, Christopher G. Atkeson Robotics Institute Carnegie Mellon University soa,jkh,cga@ri.cmu.edu
More informationGeneralizations of the Capture Point to Nonlinear Center of Mass Paths and Uneven Terrain
Generalizations of the Capture Point to Nonlinear Center of Mass Paths and Uneven Terrain Oscar E. Ramos and Kris Hauser Abstract The classical Capture Point (CP technique allows biped robots to take protective
More informationA Model-Based Control Approach for Locomotion Control of Legged Robots
Biorobotics Laboratory A Model-Based Control Approach for Locomotion Control of Legged Robots Semester project Master Program: Robotics and Autonomous Systems Micro-Technique Department Student: Salman
More informationCS 231. Control for articulate rigid-body dynamic simulation. Articulated rigid-body dynamics
CS 231 Control for articulate rigid-body dynamic simulation Articulated rigid-body dynamics F = ma No control 1 No control Ragdoll effects, joint limits RT Speed: many sims at real-time rates on today
More informationBOLETIM TECNICO DA PETROBRAS ISSN:
BOLETIM TECNICO DA PETROBRAS VOL. 61, 017 Guest Editors: Hao WANG, Min DUAN Copyright 017, PB Publishing ISBN: 978-1-94801-00-3 ISSN:0006-6117 http://www.pbpublishing.com.br EISSN:1676-6385 Design of humanoid
More informationThree-dimensional bipedal walking control using Divergent Component of Motion
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) November 3-7, 2013. Tokyo, Japan Three-dimensional bipedal walking control using Divergent Component of Motion Johannes Englsberger,
More informationMultiple Contact Planning for Minimizing Damage of Humanoid Falls
Multiple Contact Planning for Minimizing Damage of Humanoid Falls Sehoon Ha 1 and C. Karen Liu 2 Abstract This paper introduces a new planning algorithm to minimize the damage of humanoid falls by utilizing
More informationPush Recovery Control for Force-Controlled Humanoid Robots
Push Recovery Control for Force-Controlled Humanoid Robots Benjamin Stephens CMU-RI-TR-11-15 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics The
More informationControl of Humanoid Hopping Based on a SLIP Model
Control of Humanoid Hopping Based on a SLIP Model Patrick M. Wensing and David E. Orin Abstract Humanoid robots are poised to play an ever-increasing role in society over the coming decades. The structural
More informationLearning Humanoid Motion Dynamics through Sensory-Motor Mapping in Reduced Dimensional Spaces
Learning Humanoid Motion Dynamics through Sensory-Motor Mapping in Reduced Dimensional Spaces Rawichote Chalodhorn, David B. Grimes, Gabriel Y. Maganis and, Rajesh P. N. Rao Neural Systems Laboratory Department
More information