Skip to main content
SpringerLink
Log in

Predictability and Robustness in the Manipulation of Dynamically Complex Objects

  • Chapter
  • First Online:
Progress in Motor Control

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 957))

Abstract

Manipulation of complex objects and tools is a hallmark of many activities of daily living, but how the human neuromotor control system interacts with such objects is not well understood. Even the seemingly simple task of transporting a cup of coffee without spilling creates complex interaction forces that humans need to compensate for. Predicting the behavior of an underactuated object with nonlinear fluid dynamics based on an internal model appears daunting. Hence, this research tests the hypothesis that humans learn strategies that make interactions predictable and robust to inaccuracies in neural representations of object dynamics. The task of moving a cup of coffee is modeled with a cart-and-pendulum system that is rendered in a virtual environment, where subjects interact with a virtual cup with a rolling ball inside using a robotic manipulandum. To gain insight into human control strategies, we operationalize predictability and robustness to permit quantitative theory-based assessment. Predictability is quantified by the mutual information between the applied force and the object dynamics; robustness is quantified by the energy margin away from failure. Three studies are reviewed that show how with practice subjects develop movement strategies that are predictable and robust. Alternative criteria, common for free movement, such as maximization of smoothness and minimization of force, do not account for the observed data. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its analyses are a promising platform to gain insights into neurological diseases, such as dystonia and multiple sclerosis, as well as healthy aging.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Begg RK, Sparrow WA (2000) Gait characteristics of young and older individuals negotiating a raised surface: implications for the prevention of falls. J Gerontol Ser A Biol Sci Med Sci 55:M147–M154

    Article  CAS  Google Scholar 

  • Bernstein B, Hall DA, Trent HM (1958) On the dynamics of a bull whip. J Acoust Soc Am 30:1112–1115

    Article  Google Scholar 

  • Burdet E, Osu R, Franklin D, Milner T, Kawato M (2001) The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414:446–449

    Article  CAS  PubMed  Google Scholar 

  • Charles SK, Hogan N (2011) Dynamics of wrist rotation. J Biomech 44:614–621

    Article  PubMed  Google Scholar 

  • Chu WTV, Sternad D, Sanger TD (2013) Healthy and dystonic children are sensitive to their motor variability. J Neurophysiol 109:2169–2178

    Article  PubMed  PubMed Central  Google Scholar 

  • Chu WTV, Park S, Sanger TD, Sternad D (2016) Dystonic children can learn a novel motor skill: strategies that are tolerant to high variability. IEEE Trans Neural Syst Rehabil Eng 8:847–858

    Google Scholar 

  • Cluff R, Riley MA, Balasubramanian R (2009) Dynamical structure of hand trajectories during pole balancing. Neurosci Lett 464:88–92

    Article  CAS  PubMed  Google Scholar 

  • Cohen R, Sternad D (2009) Variability in motor learning: relocating, channeling and reducing noise. Exp Brain Res 193:69–83

    Article  CAS  PubMed  Google Scholar 

  • Cohen RG, Sternad D (2012) State space analysis of intrinsic timing: exploiting task redundancy to reduce sensitivity to timing. J Neurophysiol 107:618–627

    Article  PubMed  Google Scholar 

  • Colgate J, Hogan N (1988) Robust control of dynamically interacting systems. Int J Control 48:65–88

    Article  Google Scholar 

  • Cover TM, Thomas JA (2006) Elements of information theory. Wiley, Hoboken

    Google Scholar 

  • Danion F, Diamond J, Flanagan J (2012) The role of haptic feedback when manipulating nonrigid objects. J Neurophysiol 107:433–441

    Article  PubMed  Google Scholar 

  • Dijkstra TM, Katsumata H, De Rugy A, Sternad D (2004) The dialogue between data and model: passive stability and relaxation behavior in a ball bouncing task. Nonlinear Stud 11:319–344

    Google Scholar 

  • Dingwell JB, Mah CD, Mussa-Ivaldi FA (2002) Manipulating objects with internal degrees of freedom: evidence for model-based control. J Neurophysiol 88:222–235

    PubMed  Google Scholar 

  • Dingwell J, Mah C, Mussa-Ivaldi F (2004) Experimentally confirmed mathematical model for human control of a non-rigid object. J Neurophysiol 91:1158–1170

    Article  PubMed  Google Scholar 

  • Evarts E (1968) Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol 31:14–27

    CAS  PubMed  Google Scholar 

  • Faisal AA, Selen LP, Wolpert DM (2008) Noise in the nervous system. Nat Rev Neurosci 9:292–303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47:381–391

    Article  CAS  PubMed  Google Scholar 

  • Flanagan J, Wing A (1997) The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads. J Neurosci 17:1519–1528

    CAS  PubMed  Google Scholar 

  • Flanagan J, Tresilian J, Wing A (1993) Coupling of grip force and load force during arm movements with grasped objects. Neurosci Lett 152:53–56

    Article  CAS  PubMed  Google Scholar 

  • Flanagan J, Nakano E, Imamizu H, Osu R, Yoshioka T, Kawato M (1999) Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments. J Neurosci 34:1–5

    Google Scholar 

  • Flanagan J, Vetter P, Johansson R, Wolpert D (2003) Prediction precedes control in motor learning. Curr Biol 13:146–150

    Article  CAS  PubMed  Google Scholar 

  • Foo P, Kelso J, de Guzman G (2000) Functional stabilization of unstable fixed points: human pole balancing using time-to-balance information. J Exp Psychol Human Percept Perform 26:1281–1297

    Article  CAS  Google Scholar 

  • Franklin D, Osu R, Burdet E, Kawato M, Miner T (2003) Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model. J Neurophysiol 90:3270–3282

    Article  PubMed  Google Scholar 

  • Fu Q, Santello M (2014) Coordination between digit forces and positions: interactions between anticipatory and feedback control. J Neurophysiol 111:1519–1528

    Article  PubMed  Google Scholar 

  • Gao F, Latash ML, Zatsiorsky VM (2005) Internal forces during object manipulation. Exp Brain Res 165:69–83

    Article  PubMed  PubMed Central  Google Scholar 

  • Gawthrop P, Lee K-Y, Halaki M, O’Dwyer N (2013) Human stick balancing: an intermittent control explanation. Biol Cybern 107:637–652

    Article  PubMed  Google Scholar 

  • Gepshtein S, Seydell A, Trommershäuser J (2007) Optimality of human movement under natural variations of visual–motor uncertainty. J Vision 7, 13. doi:10.1167/7.5.13

    Google Scholar 

  • Goriely A, McMillen T (2002) Shape of a cracking whip. Phys Rev Lett 88:244301

    Article  PubMed  Google Scholar 

  • Hadjiosif AM, Smith MA (2015) Flexible control of safety margins for action based on environmental variability. J Neurosci 35:9106–9121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hamel KA, Okita N, Higginson JS, Cavanagh PR (2005) Foot clearance during stair descent: effects of age and illumination. Gait Posture 21:135–140

    Article  PubMed  Google Scholar 

  • Harris CM, Wolpert DM (1998) Signal-dependent noise determines motor planning. Nature 394:780–784

    Article  CAS  PubMed  Google Scholar 

  • Hasson C, Sternad D (2014) Safety margins in older adults increase with improved control of a dynamic object. Front Aging Neurosci 6, 158. doi:10.3389/fnagi.2014.00158

  • Hasson CJ, Van Emmerik RE, Caldwell GE (2008) Predicting dynamic postural instability using center of mass time-to-contact information. J Biomech 41:2121–2129

    Article  PubMed  PubMed Central  Google Scholar 

  • Hasson C, Shen T, Sternad D (2012a) Energy margins in dynamic object manipulation. J Neurophysiol 108:1349–1365

    Article  PubMed  PubMed Central  Google Scholar 

  • Hasson CJ, Hogan N, Sternad D (2012b) Human control of dynamically complex objects. In: 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp 1235–1240

    Google Scholar 

  • Hinrichsen D, Pritchard A (2005) Mathematical systems theory: modelling, state space analysis, stability and robustness. Springer, New York

    Book  Google Scholar 

  • Hof A, Gazendam M, Sinke W (2005) The condition for dynamic stability. J Biomech 38:1–8

    Article  CAS  PubMed  Google Scholar 

  • Hogan N (1985) Impedance control: An approach to manipulation: Part II—implementation. J Dyn Syst 107:8–16

    Article  Google Scholar 

  • Hogan N, Sternad D (2009) Sensitivity of smoothness measures to movement duration, amplitude, and arrests. J Motor Behav 41:529–534

    Article  Google Scholar 

  • Hogan N, Sternad D (2012) Dynamic primitives of motor behavior. Biol Cybern 106:727–739

    Article  PubMed  PubMed Central  Google Scholar 

  • Huang FC, Gillespie RB, Kuo AD (2002) Haptic feedback and human performance in a dynamic task. In: 10th symposium on haptic interfaces for virtual environmental and teleoperating systems (Haptics ’02). IEEE, Orlando, pp 24–31

    Google Scholar 

  • Huang FC, Gillespie RB, Kuo AD (2007) Visual and haptic feedback contribute to tuning and online control during object manipulation. J Motor Behav 39:179–193

    Article  Google Scholar 

  • Hudson TE, Tassinari H, Landy MS (2010) Compensation for changing motor uncertainty. PLoS Comput Biol 6:e1000982

    Article  PubMed  PubMed Central  Google Scholar 

  • Insperger T, Milton J, Stepan G (2013) Acceleration feedback improves balancing against reflex delay. J R Soc Interface 10(79), 20120763

    Google Scholar 

  • Kalaska J (2009) From intentions to actions: motor cortex and the control of reaching movements. In: Sternad D (ed) Progress in Motor Control V: a multidisciplinary approach. Springer, New York

    Google Scholar 

  • Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9:718–727

    Article  CAS  PubMed  Google Scholar 

  • Lee H, Krebs H, Hogan N (2014) Multivariable dynamic ankle mechanical impedance with active muscles. IEEE Trans Neural Syst Rehabil Eng 22:971–981

    Article  PubMed  PubMed Central  Google Scholar 

  • Leib R, Karniel A (2012) Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation. J Neurophysiol 108:1646–1655

    Article  PubMed  Google Scholar 

  • Loeb GE (2012) Optimal isn’t good enough. Biol Cybern 106:757–765

    Article  PubMed  Google Scholar 

  • Mayer HC, Krechetnikov R (2012) Walking with coffee: why does it spill? Phys Rev E 85:046117

    Article  CAS  Google Scholar 

  • McFadyen BJ, Prince F (2002) Avoidance and accommodation of surface height changes by healthy, community-dwelling, young, and elderly men. J Gerontol Ser A Biol Sci Med Sci 57:B166–B174

    Article  Google Scholar 

  • Mehta B, Schaal S (2002) Forward models in visuomotor control. J Neurophysiol 88:942–953

    PubMed  Google Scholar 

  • Milton J (2011) The delayed and noisy nervous system: implications for neural control. J Neural Eng 8:065005

    Article  PubMed  Google Scholar 

  • Milton J, Fuerte A, Bélair C, Lippai J, Kamimura A et al (2013) Delayed pursuit-escape as a model for virtual stick balancing. Nonlinear Theor Appl 4:129–137

    Google Scholar 

  • Nagengast A, Braun D, Wolpert D (2009) Optimal control predicts human performance on objects with internal degrees of freedom. PLoS Comput Biol 5:e1000419

    Article  PubMed  PubMed Central  Google Scholar 

  • Nasseroleslami B, Hasson C, Sternad D (2014) Rhythmic manipulation of objects with complex dynamics: predictability over chaos. PLoS Comput Biol 10:e1003900

    Article  PubMed  PubMed Central  Google Scholar 

  • Nowak D, Hermsdörfer J (2003) Selective deficits of grip force control during object manipulation in patients with reduced sensibility of the grasping digits. Neurosci Res 47:65–72

    Article  PubMed  Google Scholar 

  • Ogata K (2010) Modern control engineering. Prentice Hall, New York

    Google Scholar 

  • Pruszynski JA, Kurtzer I, Scott SH (2011) The long-latency reflex is composed of at least two functionally independent processes. J Neurophysiol 106:449–459

    Article  PubMed  Google Scholar 

  • Santello M, Soechting JF (2000) Force synergies for multi-fingered grasping. Exp Brain Res 133:457–467

    Article  CAS  PubMed  Google Scholar 

  • Santello M, Flanders M, Soechting J (1998) Postural hand synergies for tool use. J Neurosci 18:10105–10115

    CAS  PubMed  Google Scholar 

  • Sauret A, Boulogne F, Cappello J, Dressaire E, Stone H (2015) Damping of liquid sloshing by foams. Phys Fluids 27:022103

    Article  Google Scholar 

  • Scheidt R, Dingwell J, Mussa-Ivaldi F (2001) Learning to move amid uncertainty. J Neurophysiol 86:971–985

    CAS  PubMed  Google Scholar 

  • Selen L, Franklin D, Wolpert D (2009) Impedance control reduces instability that arises from motor noise. J Neurosci 29:12606–12616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208–3224

    CAS  PubMed  Google Scholar 

  • Slobounov SM, Moss SA, Slobounova ES, Newell KM (1998) Aging and time to instability in posture. J Gerontol Ser A Biol Sci Med Sci 53:B71–B80

    Article  CAS  Google Scholar 

  • Sternad D, Abe MO, Hu X, Muller H (2011) Neuromotor noise, error tolerance and velocity-dependent costs in skilled performance. PLoS Comput Biol 7:e1002159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sternad D, Huber ME, Kuznetsov N (2014) Acquisition of novel and complex motor skills: stable solutions where intrinsic noise matters less. Adv Exp Med Biol 826:101–124

    Article  PubMed  Google Scholar 

  • Svinin M, Goncharenko I, Luo Z-W, Hosoe S (2006) Reaching movements in dynamic environments: how do we move flexible objects? IEEE Trans Robot 22:724–739

    Article  Google Scholar 

  • Takahashi C, Scheidt R, Reinkensmeyer D (2001) Impedance control and internal model formation when reaching in a randomly varying dynamical environment. J Neurophysiol 86:1047–1051

    CAS  PubMed  Google Scholar 

  • Trommershäuser J, Gepshtein S, Maloney LT, Landy MS, Banks MS (2005) Optimal compensation for changes in task-relevant movement variability. J Neurosci 25:7169–7178

    Article  PubMed  Google Scholar 

  • van der Linde R, Lammertse P (2003) HapticMaster—a generic force controlled robot for human interaction. Ind Robot Int J 30:515–524

    Article  Google Scholar 

  • Van Wegen E, Van Emmerik R, Riccio G (2002) Postural orientation: age-related changes in variability and time-to-boundary. Human Mov Sci 21:61–84

    Article  Google Scholar 

  • Venkadesan M, Guckenheimer J, Valero-Cuevas F (2007) Manipulating the edge of instability. J Biomech 40:1653–1661

    Article  PubMed  PubMed Central  Google Scholar 

  • Zatsiorsky VM (1998) Kinematics of human motion. Human Kinetics, Champaign, IL

    Google Scholar 

  • Zhou K, Doyle JC (1998) Essentials of robust control. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

Download references

Acknowledgments

This work was supported by the NIH-R01-HD045639, NIH-R01-HD081346, NIH-R21-DC013095, and NSF-EAGER-1548514 awarded to Dagmar Sternad and NIH-F32-AR061238 awarded to Christopher J. Hasson.

Author information

Authors and Affiliations

  1. Department of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, 134 Mugar Life Science Building, 360 Huntington Avenue, Boston, Massachusetts, 02115, USA

    Dagmar Sternad & Christopher J. Hasson

  2. Department of Physical Therapy, Movement and Rehabilitation Sciences, Department of Biology, Northeastern University, Boston, Massachusetts, USA

    Christopher J. Hasson

Authors
  1. Dagmar Sternad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher J. Hasson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dagmar Sternad .

Editor information

Editors and Affiliations

  1. Department of Information Technology and Biorobotics, Faculty of Science, University of Pécs, Pecs, Hungary

    Jozsef Laczko

  2. Department of Kinesiology, Pennsylvania State University, University Park, Pennsylvania, USA

    Mark L. Latash

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this chapter

Cite this chapter

Sternad, D., Hasson, C.J. (2016). Predictability and Robustness in the Manipulation of Dynamically Complex Objects. In: Laczko, J., Latash, M. (eds) Progress in Motor Control. Advances in Experimental Medicine and Biology, vol 957. Springer, Cham. https://doi.org/10.1007/978-3-319-47313-0_4

Download citation

Publish with us

Policies and ethics

Search

Navigation