, 43:28 | Cite as

An engineering-design oriented exploration of human excellence in throwing

  • Susheel Dharmadhikari
  • Anindya Chatterjee


Humans are fast throwers, and their bodies differ correspondingly from those of other hominids. One might ask why humans evolved to throw fast while others did not; whether the design of a fast thrower is unique or special and whether indeed humans remain fast within broader comparison sets of non-hominid throwers. As a non-hominid comparison set, we consider a random population of five-link robots with simplified joint angle and torque constraints. We generate 20,000 such robot models and sequentially optimize their throwing motion. Since good initial guesses are needed for each optimization, the robots are first arranged in distance-minimizing sequences in design parameter space. Each robot’s optimal throw then serves as an initial guess for the next one in sequence. Multiple traversals of these sequences, and random perturbations, are used to avoid local optima. Subsequently, regression models are used to predict throwing performance as a function of robot design parameters. From these regression models, the dominant heuristic predictor of fast throwing is found to be a long and light last link. Direct optimization of the robot design leads to much faster throwers, also with long and light last links. In striking contrast, the human arm has two equally long intermediate links of significant mass. Nevertheless, a somewhat human-like arm within the same robot set is found to be a good thrower. On combining several throwing criteria to obtain a single figure of merit, the human-like arm lies in the 96th percentile of the population. Since our human-like arm is a crude approximation of an actual human arm, we suggest that fast throwing by human-like robot arms is not inherently difficult from a mechanical point of view.


Human arm throwing robots optimization statistics 



We thank Sourav Rakshit, Taher Saif, Anurag Gupta, Devlina Chatterjee, Jim Papadopoulos, Sovan Das, Dagmar Sternad and Joe Cusumano for discussion and comments.

Supplementary material


  1. 1.
    Roach N T, Venkadesan M, Rainbow M J and Lieberman D E 2013 Elastic energy storage in the shoulder and the evolution of high-speed throwing in Homo. Nature 498(7455): 483–486CrossRefGoogle Scholar
  2. 2.
    Young R W 2003 Evolution of the human hand: the role of throwing and clubbing. J. Anat. 203(1): 165–174CrossRefGoogle Scholar
  3. 3.
    Wood J N, Glynn D D and Hauser M D 2007 The uniquely human capacity to throw evolved from a non-throwing primate: an evolutionary dissociation between action and perception. Biol. Lett. 3: 360–364CrossRefGoogle Scholar
  4. 4.
    Hopkins W D, Russell J L and Schaeffer J A 2011 The neural and cognitive correlates of aimed throwing in chimpanzees: a magnetic resonance image and behavioural study on a unique form of social tool use. Philos. Trans. R. Soc. B 367: 37–47CrossRefGoogle Scholar
  5. 5.
    Fleisig G S, Barrentine S W, Escamilla R F and Andrews J R 1996 Biomechanics of overhand throwing with implications for injuries. Sports Med. 21(6): 421–437CrossRefGoogle Scholar
  6. 6.
    Cain E L, Dugas J R, Wolf R S and Andrews J R 2003 Elbow injuries in throwing athletes: a current concepts review. Am. J. Sports Med. 31(4): 621–635CrossRefGoogle Scholar
  7. 7.
    Braun S, Kokmeyer D and Millett P J 2009 Shoulder injuries in the throwing athlete. J. Bone Joint Surg. 91(4): 966–978CrossRefGoogle Scholar
  8. 8.
    Lombai F and Szederkényi G 2009 Throwing motion generation using nonlinear optimization on a 6-degree-of-freedom robot manipulator. In: Proceedings of the IEEE International Conference on Mechatronics, pp. 1–6Google Scholar
  9. 9.
    Kato N, Matsuda K and Nakamura T 1996 Adaptive control for a throwing motion of a 2-dof robot. In: Proceedings of 4th International Workshop on Advanced Motion Control, vol. 1, pp. 203–207Google Scholar
  10. 10.
    Sato A, Sato O, Takahashi N and Kono M 2007 Trajectory for saving energy of a direct-drive manipulator in throwing motion. Artif. Life Robot. 11(1): 61–66CrossRefGoogle Scholar
  11. 11.
    Senoo T, Namiki A and Ishikawa M 2008 High-speed throwing motion based on kinetic chain approach. In: IEEE/RSJ Proceedings of International Conference on Intelligent Robots and Systems, pp. 3206–3211Google Scholar
  12. 12.
    Kim J H, Xiang Y, Yang J, Arora J S and Abdel-Malek K 2010 Dynamic motion planning of overarm throw for a biped human multibody system. Multibody Syst. Dyn. 24(1): 1–24MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Hussain I and Bari M A 2011 Mechanical analysis of overhead throwing in cricket. Int. J. Sports Sci. Eng. 5(3): 163–168Google Scholar
  14. 14.
    Fleisig G 2001 The biomechanics of throwing. In: Proceedings of the International Symposium on Biomechanics in Sports, vol. 1(1), pp. 91–94Google Scholar
  15. 15.
    Berkson E, Aylward R, Zachazewski J, Paradiso J and Gill T J 2006 IMU arrays: the biomechanics of baseball pitching. Orthop. J. Harv. Med. School 8: 90–94Google Scholar
  16. 16.
    Escamilla R F, Fleisig G S, Barrentine S W, Zheng N and Andrews J R 1998 Kinematic comparisons of throwing different types of baseball pitches. J. Appl. Biomech. 14: 1–23CrossRefGoogle Scholar
  17. 17.
    Kernighan B W and Lin S 1970 An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2): 291–307CrossRefzbMATHGoogle Scholar
  18. 18.
    Dawes R M 1979 The robust beauty of improper linear models in decision making. Am. Psychol. 34(7): 571–582CrossRefGoogle Scholar
  19. 19.
    Plagenhoef S, Evans F G and Abdelnour T 1983 Anatomical data for analyzing human motion. Res. Q. Exerc. Sport 54(2): 169–178CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Structural Dynamics TeamEaton TechnologiesPuneIndia
  2. 2.Department of Mechanical EngineeringIndian Institute of TechnologyKanpurIndia

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