Towards a learnt neural body schema for dexterous coordination of action in humanoid and industrial robots
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During any goal oriented behavior the dual processes of generation of dexterous actions and anticipation of the consequences of potential actions must seamlessly alternate. This article presents a unified neural framework for generation and forward simulation of goal directed actions and validates the architecture through diverse experiments on humanoid and industrial robots. The basic idea is that actions are consequences of an simulation process that animates the internal model of the body (namely the body schema), in the context of intended goals/constraints. Specific focus is on (a) Learning: how the internal model of the body can be acquired by any robotic embodiment and extended to coordinated tools; (b) Configurability: how diverse forward/inverse models of action can be ‘composed’ at runtime by coupling/decoupling different body (body \(+\) tool) chains with task relevant goals and constraints represented as multi-referential force fields; and (c) Computational simplicity: how both the synthesis of motor commands to coordinate highly redundant systems and the ensuing forward simulations are realized through well-posed computations without kinematic inversions. The performance of the neural architecture is demonstrated through a range of motor tasks on a 53-DoFs robot iCub and two industrial robots performing real world assembly with emphasis on dexterity, accuracy, speed, obstacle avoidance, multiple task-specific constraints, task-based configurability. Putting into context other ideas in motor control like the Equilibrium Point Hypothesis, Optimal Control, Active Inference and emerging studies from neuroscience, the relevance of the proposed framework is also discussed.
KeywordsBody schema Passive motion paradigm iCub Motor control Industrial assembly
This work presented in this article is supported by Robotics, Brain and Cognitive Sciences Department IIT, the EU FP7 Project DARWIN (www.darwin-project.eu, Grant No. FP7-270138) and US Dept. of Defense Grant (W911QY-12-C0078).
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- Asatryan, D. G., & Feldman, A. G. (1965). Functional tuning of the nervous system with control of movements or maintenance of a steady posture. Biophysics, 10, 925–935.Google Scholar
- Balestrino, A., De Maria, G., & Sciavicco, L. (1984). Robust control of robotic manipulators. In Proceedings of the 9th IFAC world congress (Vol. 5, pp. 2435–2440).Google Scholar
- Bekey, G., & Goldberg, K. Y. (Eds.). (2012). Neural networks in robotics (Vol. 202). Berlin: Springer.Google Scholar
- Bernstein, N. (1935). The problem of the interrelationships between coordination and localization. Retrieved November 13th, 2015 from http://www.cns.nyu.edu/~bijan/courses/sm10/Readings/Glimcher/Problem%20of%20the%20Interrelation%20of%20Coor%20and%20Local%20-%20PGArt.pdf.
- Bernstein, N. (1967). The coordination and regulation of movements. Oxford: Pergamon Press.Google Scholar
- Bhat, A. A., & Mohan, V. (2015). How iCub learns to imitate use of a tool quickly by recycling the past knowledge learnt during drawing. In Biomimetic and biohybrid systems (pp. 339–347). Berlin: Springer.Google Scholar
- Bryson, E. (1999). Dynamic optimization. Menlo Park, CA: Addison Wesley Longman.Google Scholar
- Cai, H., Werner, T., & Matas, J. (2013). Fast detection of multiple textureless 3-D objects. In Computer vision systems (pp. 103–112). Berlin: Springer.Google Scholar
- DARWIN D9.4. (2014). Deliverable D9.4: Third year demonstrators and evaluation report. EC FP7 project DARWIN Grant No. 270138. Retrieved November 10th, 2015 from http://darwin-project.eu/wp-content/uploads/2010/07/D94_Y3_Demonstrators_Evaluation_v3.0.pdf.
- DARWIN D9.5. (2015). Deliverable D9.5: Industrial assembly demonstrator and final evaluation. EC FP7 project DARWIN Grant No. 270138. Retrieved November 10th, 2015 from http://darwin-project.eu/wp-content/uploads/2010/07/D95_Y4_Demonstrators_Evaluation.pdf.
- De Luca, A., & Oriolo, G. (1991). Issues in acceleration resolution of robot redundancy. In Third IFAC symposium on robot control (pp. 93–98).Google Scholar
- De Luca, A., Oriolo, G., & Siciliano, B. (1992). Robot redundancy resolution at the acceleration level. Laboratory Robotics and Automation, 4, 97–106.Google Scholar
- Featherstone, R. (1987). Robot Dynamics Algorithms. Dordrecht: Kluwer.Google Scholar
- Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience, 5, 1688–1703.Google Scholar
- Graziano, M. S. A., & Botvinick, M. M. (2002). How the brain represents the body: Insights from neurophysiology and psychology. In W. Prinz & B. Hommel (Eds.), Common mechanisms in perception and action: Attention and performance (pp. 136–157). Oxford: Oxford University Press.Google Scholar
- Guigon, E. (2011). Models and architectures for motor control: Simple or complex? In F. Danion & M. L. Latash (Eds.), Motor control (pp. 478–502). Oxford: Oxford University Press.Google Scholar
- Haggard, P., & Wolpert, D. M. (2005). Disorders of body schema. In H. J. Freund, M. Jeannerod, M. Hallett, & R. Leiguarda (Eds.), Higher-order motor disorders: From neuroanatomy and neurobiology to clinical neurology (pp. 261–271). Oxford: Oxford University Press.Google Scholar
- Jordan, M. I. (1990). Motor learning and the degrees of freedom problem. In M. Jeannerod (Ed.), Attention and performance XIII. Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
- Lashley, K. S. (1933). Integrative function of the cerebral cortex. Physiological Reviews, 13(1), 1–42.Google Scholar
- Lee, S., & Kil, R. M. (1990, June). Robot kinematic control based on bidirectional mapping neural network. In 1990 IJCNN international joint conference on neural networks, 1990 (pp. 327–335). New York: IEEE.Google Scholar
- Lewis, F. W., Jagannathan, S., & Yesildirak, A. (1998). Neural network control of robot manipulators and non-linear systems. Boca Raton: CRC Press.Google Scholar
- Lourakis, M., & Zabulis, X. (2013). Model-based pose estimation for rigid objects. In Computer vision systems (pp. 83–92). Berlin: Springer.Google Scholar
- Mel, B. W. (1988). MURPHY: A robot that learns by doing. In Neural information processing systems (pp. 544–553).Google Scholar
- Mohan, V., & Morasso, P. (2011). Passive motion paradigm: An alternative to optimal control. Frontiers in Neurorobotics, 5, 4.Google Scholar
- Mohan, V., Morasso, P., Zenzeri, J., Metta, G., Chakravarthy, V. S., & Sandini, G. (2011). Teaching a humanoid robot to draw ‘Shapes’. Autonomous Robots, 31(1), 21–53.Google Scholar
- Mussa-Ivaldi, F. A., Morasso, P., & Zaccaria, R. (1988). Kinematic networks. A distributed model for representing and regularizing motor redundancy. Biological Cybernetics, 60, 1–16.Google Scholar
- Nguyen, L., Patel, R. V., & Khorasani, K. (1990, June). Neural network architectures for the forward kinematics problem in robotics. In 1990 IJCNN international joint conference on neural networks (pp. 393–399). New York: IEEE.Google Scholar
- Salaün, C., Padois, V., & Sigaud, O. (2009, October). Control of redundant robots using learned models: An operational space control approach. In IROS 2009 IEEE/RSJ international conference on intelligent robots and systems, 2009 (pp. 878–885). New York: IEEE.Google Scholar
- Senda, K. (1999). Quasioptimal control of space redundant manipulators. AIAA Guidance, Navigation, and Control Conference, 3, 1877–1885.Google Scholar
- Todorov, E. (2006). Optimal control theory. In K. Doya, et al. (Eds.), Bayesian brain: Probabilistic approaches to neural coding (pp. 269–298). Cambridge, MA: MIT Press.Google Scholar
- Wolovich, W. A., & Elliot, H. (1984). A computational technique for inverse kinematics. In Proceedings of the 23rd IEEE conference on decision and control (pp. 1359–1363).Google Scholar