Research Papers

Deriving Humanlike Arm Hand System Poses

[+] Author and Article Information
Minas Liarokapis

School of Engineering and Applied Science,
Yale University,
9 Hillhouse Avenue,
New Haven, CT 06511
e-mail: minas.liarokapis@yale.edu

Charalampos P. Bechlioulis

School of Mechanical Engineering,
National Technical University of Athens,
Athens 15780, Greece
e-mail: chmpechl@mail.ntua.gr

Panagiotis K. Artemiadis

School for Engineering of Matter,
Transport and Energy,
Arizona State University,
Tempe, AZ 85287
e-mail: panagiotis.artemiadis@asu.edu

Kostas J. Kyriakopoulos

School of Mechanical Engineering,
National Technical University of Athens,
Athens 15780, Greece
e-mail: kkyria@mail.ntua.gr

1Corresponding author.

Manuscript received May 30, 2016; final manuscript received December 10, 2016; published online January 9, 2017. Assoc. Editor: Marcia K. O'Malley.

J. Mechanisms Robotics 9(1), 011012 (Jan 09, 2017) (9 pages) Paper No: JMR-16-1156; doi: 10.1115/1.4035505 History: Received May 30, 2016; Revised December 10, 2016

Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to “teach” robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinematics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Epley, N. , Waytz, A. , and Cacioppo, J. , 2007, “ On Seeing Human: A Three-Factor Theory of Anthropomorphism,” Psychol. Rev., 114(4), pp. 864–886. [CrossRef] [PubMed]
Liarokapis, M. V. , Artemiadis, P. K. , and Kyriakopoulos, K. J. , 2012, “ Functional Anthropomorphism for Human to Robot Motion Mapping,” 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Paris, Sept. 9–12, pp. 31–36. http://horc.engineering.asu.edu/HORC/PaperDir/2012/ROMAN12_Liarokapis.pdf
Duffy, B. R. , 2003, “ Anthropomorphism and the Social Robot,” Rob. Auton. Syst., 42(3), pp. 177–190. [CrossRef]
Gielniak, M. J. , Liu, C. K. , and Thomaz, A. L. , 2013, “ Generating Human-Like Motion for Robots,” Int. J. Rob. Res., 32(11), pp. 1275–1301. [CrossRef]
Beetz, M. , Stulp, F. , Esden-Tempski, P. , Fedrizzi, A. , Klank, U. , Kresse, I. , Maldonado, A. , and Ruiz, F. , 2010, “ Generality and Legibility in Mobile Manipulation,” Auton. Rob., 28(1), pp. 21–44. [CrossRef]
Alami, R. , Clodic, A. , Montreuil, V. , Sisbot, E. A. , and Chatila, R. , 2006, “ Toward Human-Aware Robot Task Planning,” AAAI Spring Symposium: To Boldly Go Where No Human-Robot Team Has Gone Before, pp. 39–46. http://www.aaai.org/Papers/Symposia/Spring/2006/SS-06-07/SS06-07-006.pdf
Dragan, A. , and Srinivasa, S. , 2013, “ Generating Legible Motion,” Robotics: Science and Systems, Berlin, June.
Speeter, T. H. , 1992, “ Transforming Human Hand Motion for Telemanipulation,” Presence: Teleoper. Virtual Environ., 1(1), pp. 63–79. [CrossRef]
Ciocarlie, M. , Goldfeder, C. , and Allen, P. , 2007, “ Dimensionality Reduction for Hand-Independent Dexterous Robotic Grasping,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, Oct. 29–Nov. 2, pp. 3270–3275. https://pdfs.semanticscholar.org/ba29/4b627f271fa6f01ed19af591172612280cf7.pdf
Pao, L. , and Speeter, T. , 1989, “ Transformation of Human Hand Positions for Robotic Hand Control,” IEEE International Conference on Robotics and Automation (ICRA), Scottsdale, AZ, May 14–19, Vol. 3, pp. 1758–1763.
Griffin, W. B. , Findley, R. P. , Turner, M. L. , and Cutkosky, M. R. , 2000, “ Calibration and Mapping of a Human Hand for Dexterous Telemanipulation,” ASME IMECE Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, pp. 1–8. http://www-cdr.stanford.edu/dml/publications/griffin_asme00.pdf
Gioioso, G. , Salvietti, G. , Malvezzi, M. , and Prattichizzo, D. , 2013, “ Mapping Synergies From Human to Robotic Hands With Dissimilar Kinematics: An Approach in the Object Domain,” IEEE Trans. Rob., 29(4), pp. 825–837. [CrossRef]
Toshani, H. , and Farrokhi, M. , 2014, “ Real-Time Inverse Kinematics of Redundant Manipulators Using Neural Networks and Quadratic Programming: A Lyapunov-Based Approach,” Rob. Auton. Syst., 62(6), pp. 766–781. [CrossRef]
Artemiadis, P. K. , Katsiaris, P. T. , and Kyriakopoulos, K. J. , 2010, “ A Biomimetic Approach to Inverse Kinematics for a Redundant Robot Arm,” Auton. Rob., 29(3–4), pp. 293–308. [CrossRef]
Chirikjian, G. , and Burdick, J. , 1995, “ Kinematically Optimal Hyper-Redundant Manipulator Configurations,” IEEE Trans. Rob. Autom., 11(6), pp. 794–806. [CrossRef]
Fromherz, M. P. J. , and Jackson, W. , 2000, “ Predictable Motion of Hyper-Redundant Manipulators Using Constrained Optimization Control,” International Conference on Artificial Intelligence (IC-AI), Las Vegas, June 21–26, pp. 1141–1147. http://www2.parc.com/isl/members/fromherz/publications/sm-icai00.pdf
Engell-Nørregård, M. , and Erleben, K. , 2011, “ A Projected Back-Tracking Line-Search for Constrained Interactive Inverse Kinematics,” Comput. Graphics, 35(2), pp. 288–298. [CrossRef]
Zhao, J. , and Badler, N. I. , 1994, “ Inverse Kinematics Positioning Using Nonlinear Programming for Highly Articulated Figures,” ACM Trans. Graphics, 13(4), pp. 313–336. [CrossRef]
Zacharias, F. , Schlette, C. , Schmidt, F. , Borst, C. , Rossmann, J. , and Hirzinger, G. , 2011, “ Making Planned Paths Look More Human-Like in Humanoid Robot Manipulation Planning,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, May 9–13, pp. 1192–1198.
Albrecht, S. , Ramirez-Amaro, K. , Ruiz-Ugalde, F. , Weikersdorfer, D. , Leibold, M. , Ulbrich, M. , and Beetz, M. , 2011, “ Imitating Human Reaching Motions Using Physically Inspired Optimization Principles,” IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, Oct. 26–28, pp. 602–607.
Atawnih, A. , Papageorgiou, D. , and Doulgeri, Z. , 2014, “ Reaching for Redundant Arms With Human-Like Motion and Compliance Properties,” Rob. Auton. Syst., 62(12), pp. 1731–1741. [CrossRef]
e Silva, E. C. , Costa, F. , Bicho, E. , and Erlhagen, W. , 2011, “ Nonlinear Optimization for Human-Like Movements of a High Degree of Freedom Robotics Arm-Hand System,” International Conference on Computational Science and its Applications—Volume Part III (ICCSA), pp. 327–342. https://repositorium.sdum.uminho.pt/bitstream/1822/12905/1/ECostaSilva_et_al_EB_ICCSA2011_LN_springer_in_Proceedings.pdf
Suárez, R. , Rosell, J. , and Garcia, N. , 2015, “ Using Synergies in Dual-Arm Manipulation Tasks,” IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, May 26–30, pp. 5655–5661.
Gielniak, M. J. , and Thomaz, A. L. , 2011, “ Spatiotemporal Correspondence as a Metric for Human-Like Robot Motion,” Sixth International Conference on Human-Robot Interaction (ACM/IEEE), Lausanne, Switzerland, Mar. 6–9, pp. 77–84.
Liarokapis, M. , Artemiadis, P. , and Kyriakopoulos, K. , 2013, “ Mapping Human to Robot Motion With Functional Anthropomorphism for Teleoperation and Telemanipulation With Robot Arm Hand Systems,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Nov. 3–8, p. 2075.
Pitarch, E. P. , 2007, “ Virtual Human Hand: Grasping Strategy and Simulation,” Ph.D. thesis, Universita Politechnica de Catalunya, Barcelona, Spain. https://upcommons.upc.edu/bitstream/handle/2117/94313/TEPP1de1.pdf
Liarokapis, M. , Artemiadis, P. , and Kyriakopoulos, K. , 2013, “ Quantifying Anthropomorphism of Robot Hands,” IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, May 6–10, pp. 2041–2046.
Poston, A. , 2000, Human Engineering Design Data Digest: Human Factors Standardization Systems/Human Factors Standardization SubTAG, The Group, Washington, DC.
LaValle, S. M. , 2006, Planning Algorithms, Cambridge University Press, Cambridge, UK.
Barber, C. B. , Dobkin, D. P. , and Huhdanpaa, H. , 1996, “ The Quickhull Algorithm for Convex Hulls,” ACM Trans. Math. Software, 22(4), pp. 469–483. [CrossRef]
Lawrence, J. , 1991, “ Polytope Volume Computation,” Math. Comput., 57(195), pp. 259–271. [CrossRef]
Bueler, B. , Enge, A. , and Fukuda, K. , 1997, “ Exact Volume Computation for Polytopes: A Practical Study,” Polytopes—Combinatorics and Computation, Springer, Basel, pp. 131–154.
Liarokapis, M. V. , Artemiadis, P. K. , Bechlioulis, C. P. , and Kyriakopoulos, K. J. , 2013, “ Directions, Methods and Metrics for Mapping Human to Robot Motion With Functional Anthropomorphism: A Review,” School of Mechanical Engineering, National Technical University of Athens.
Liarokapis, M. V. , 2014, “ EMG Based Interfaces for Human Robot Interaction in Structured and Dynamic Environments,” Ph.D. thesis, National Technical University of Athens, Athens, Greece. http://www.minasliarokapis.com/2014_Liarokapis_PhDThesis.pdf
Townsend, W. , 2000, “ The Barretthand Grasper-Programmably Flexible Part Handling and Assembly,” Ind. Rob.: Int. J., 27(3), pp. 181–188. [CrossRef]
Hong, J. , and Tan, X. , 1989, “ Calibrating a VPL Dataglove for Teleoperating the Utah/MIT Hand,” IEEE International Conference on Robotics and Automation (ICRA), Scottsdale, AZ, May 14–19, Vol. 3, pp. 1752–1757.
Arbib, M. , Iberall, T. , and Lyons, D. , 1985, “ Coordinated Control Programs for Movements of the Hand,” Hand Function and the Neocortex, Goodwin, A. W. and Darian-Smith, I., eds., Springer-Verlag, Berlin Heidelberg, pp. 111–129.
Liu, J. , and Zhang, Y. , 2007, “ Mapping Human Hand Motion to Dexterous Robotic Hand,” IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China, Dec. 15–18, pp. 829–834.
Boutselis, G. , Bechlioulis, C. , Liarokapis, M. , and Kyriakopoulos, K. , 2014, “ An Integrated Approach Towards Robust Grasping With Tactile Sensing,” IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, May 31–June 7, pp. 3682–3687.
Boutselis, G. , Bechlioulis, C. , Liarokapis, M. , and Kyriakopoulos, K. , 2014, “ Task Specific Robust Grasping for Multifingered Robot Hands,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, Sept. 14–18, pp. 858–863.
Corke, P. , 2007, “ MATLAB Toolboxes: Robotics and Vision for Students and Teachers,” IEEE Rob. Autom. Mag., 14(4), pp. 16–17. [CrossRef]
Johnson, S. G. , 2013, “ The NLopt Nonlinear-Optimization Package,” http://ab-initio.mit.edu/nlopt
Diankov, R. , and Kuffner, J. , 2008, “ Openrave: A Planning Architecture for Autonomous Robotics,” Robotics Institute, Pittsburgh, PA, Technical Report No. CMU-RI-TR-08-34. https://pdfs.semanticscholar.org/c28d/3dc33b629916a306cc58cbff05dcd632d42d.pdf
Smith, F. , and Rooks, B. , 2006, “ The Harmonious Robot,” Ind. Rob.: Int. J., 33(2), pp. 125–130. [CrossRef]


Grahic Jump Location
Fig. 1

Illustrations of the proposed metrics of anthropomorphism. Human arm is the right kinematic chain that consists of two links, while the hypothetical robot arm is the left kinematic chain that has 5 links.

Grahic Jump Location
Fig. 6

(a) A trajectory tracking example that involves a 20DOF hyper-redundant robot arm, the end-effector of which should attain same position and orientation with the human arm end-effector. (b) A trajectory tracking example that involves a hyper-redundant robot arm-hand system that consists of a 23DOF hyper-redundant arm and a five-fingered hand with size equal to the 110% of the human hand size. For this example, the robot fingertips should track the human fingertip positions. The lines denote the human trajectories, and the markers the derived robot trajectories.

Grahic Jump Location
Fig. 2

Mapping human to robot motion experiments. The proposed methodology has been used to extract humanlike robot poses for three different applications. (a) The real-time teleoperation of a simulated Mitsubishi PA10–DLR/HIT II arm-hand system. (b) The teleoperation of a simulated arm-hand system that consists of a hyper-redundant robot arm (21DOF) and the DLR/HIT II robot hand. (c) An example of autonomous, anthropomorphic grasp planning using the Mitsubishi PA10–DLR/HIT II arm-hand system.

Grahic Jump Location
Fig. 3

Comparison of different mapping methodologies for the case of the Barrett WAM robot arm [44]. The left kinematic chain for all cases is the Barrett WAM robot arm which is longer that then human arm (right kinematic chain). The two kinematic chains are depicted with an offset in the x-axis of their base frames, in order to facilitate comparisons. The sphere denotes the desired end-effector position for the robot arm. For all cases, the human arm pose is the same.

Grahic Jump Location
Fig. 4

Deriving humanlike poses for m-fingered robot hands with size equal to the 110% of the human hand size. The selection of the desired robot fingertip positions (crosses) is performed via interpolation between the human fingertips positions (circles).

Grahic Jump Location
Fig. 5

Deriving humanlike robot poses for (1) a 18DOF hyper-redundant arm and (2) an arm-hand system that consists of a 44DOF arm and a five-fingered hand

Grahic Jump Location
Fig. 7

Mapping human to robot motion for hyper-redundant robot arms with 21DOF and total length equal to the 80%, 90%, 100%, 110%, and 120% of the total human arm length. HA is the human arm.

Grahic Jump Location
Fig. 8

Solutions of the mapping problem for a 21DOF robot arm (that has the same length as the human arm) and different terms included in the objective function



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In