How Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project
- 161 Downloads
Flexible, robust, precise, adaptive, compliant and safe: these are some of the qualities robots must have to interact safely and productively with humans. Yet robots are still nowadays perceived as too rigid, clumsy and not sufficiently adaptive to work efficiently in interaction with people. The AMARSi Project endeavors to design and implement rich motor skills, unique flexibility, compliance and state-of-the-art learning in robots. Inspired by human-recorded motion and learning behavior, similarly versatile and constantly adaptive movements and skills endow robots with singularly human-like motor dynamics and learning. The AMARSi challenge is to integrate novel biological notions, advanced learning algorithms and cutting-edge compliant mechanics in the design of fully-fledged humanoid and quadruped robots with an unprecedented aptitude for integrating in our environments.
KeywordsAdaptive behavior Compliant systems Learning Robotics
Compared to animals and humans, the motor skills of today’s robots are still poor. Their movements are still perceived by people as abrupt, unpredictable or unnatural. In fact, the behavior of robots is often limited to a narrow set of carefully programmed motor patterns that operate a rigid mechanics and display limited adaptation to complex, task-oriented behavioral patterns. On the other hand, the smoothness, efficiency, elegance and safety of movements of humans and other animals are aspects that make the human-to-human or human-to-animal interaction still qualitatively superior to that of any robot.
These challenging research objectives are pursued by a consortium of twelve international research laboratories from the fields of bio-robotics, robot engineering, compliant mechanics, morphological computing, human motor research & bio-mechanics, theoretical biology, machine learning, neural networks & reservoir computing. The challenges of merging these different areas are tackled by an intense collaboration programme encompassing seven work packages shared across all research laboratories.
2 AMARSi Towards its Objectives
The AMARSi project started in March 2010 and is currently progressing towards its objectives in three main fields: biology, mechanics and algorithms.
The research on human primitives is contributing to the study of human motion [1, 14], from skills in new born babies , to gate transition [15, 37] and recovery , crawling , anatomy and posture , muscle synergies , catching , motor neuron oscillations , spatio-temporal tuning  and control behaviors . These studies cast new light on our understanding of how humans learn new motor skills and eventually perform complex and accurate movements. These notions are used in the design of robotics platforms and control algorithms.
Morphological computation allows robots to perform movements naturally and efficiently. Progress in AMARSi contributes to morphological computation both theoretically [12, 33] and in robotic design [7, 13], in particular with compliant actuators [27, 36] and evolutionary-designed body parts .
While biological notions and hardware components are rapidly developing, the control architectures and learning algorithms are faced with increasingly complex tasks. In AMARSi the application of learning to robotics has further advanced, in particular imitation learning [11, 19], learning of nonlinear systems [17, 18] and learning of motion dynamics for catching .
The AMARSi partners report ongoing developments currently submitted for publication in all work-packages, Human Motor Primitives, Compliant Systems, Morphological Computation, Adaptive Modules, Learning, Architectures and Software. Public deliverables, open source software, publications and other support material like images and videos are constantly updated on the project website http://amarsi-project.eu.
Rich motor skills promise to change the role of robots in our society. Progress in AMARSi shows that robots are becoming less clumsy and are beginning to show increasingly more accurate, more flexible and richer motor behaviors. Such an extended range of behaviors and skills allows robots to perform increasingly complex tasks in diverse human environments like homes and offices. More importantly, smooth and efficient motion under a variety of conditions gift robots with unprecedented human and animal-like resemblance. The contribution of this research project to natural, interactive and safe movements make robots ready to blend into the everyday routines of human society.
- 2.Chiovetto E (2011) The motor system plays the violin: a musical metaphor inferred from the oscillatory activity of the alpha-motoneuron pools during locomotion. J Neurophysiol Google Scholar
- 3.Cesqui B, d’Avella A, Portone A, Lacquaniti F (2012) Catching a ball at the right time and place: individual factors matter. PLoS One Google Scholar
- 6.d’Avella A, Portone A, Lacquaniti F (2011) Superposition and modulation of muscle synergies for reaching in response to a change in target location. J Neurophysiol Google Scholar
- 7.Dermitzakis K, Carbajal J, Marden J (2011) Scaling laws in robotics. In: The European future technologies conference and exhibition Google Scholar
- 8.Dominici N, Ivanenko Y, Cappellini G, d’Avella A., V., M., Chicchese M., Fabiano A., Sile T., Di Paolo A., Giannini C., Poppele R., Lacquaniti F. (2011) Locomotor primitives in newborn babies and their development. Science 334 Google Scholar
- 9.Freire A, Lemme A, Steil JJ, Barreto G (2012) Learning visuo-motor coordination for pointing without depth calculation. In: European symposium on artificial neural networks, computational intelligence and machine learning Google Scholar
- 10.Gan DM, Tsagarakis NG, Dai JS, Caldwell DG (2011) Joint stiffness tuning for compliant robots: protecting the robot under accidental impacts. In: 13th world congress in mechanism and machine science Google Scholar
- 11.Grollman DH, Billard A (2011) Donut as I do: learning from failed demonstrations. In: International conference on robotics and automation, Shanghai Google Scholar
- 14.Ivanenko Y, Dominici N, Daprati E, Nico D, Cappellini G, Lacquaniti F (2010) Locomotor body scheme. Human Movement Science Google Scholar
- 16.Jones HB, Soltoggio A, Sendoff B, Yao X (2011) Evolution of neural symmetry and its coupled alignment to body plan morphology. In: Proceedings of the genetic and evolutionary computation conference Google Scholar
- 18.Khansari-Zadeh SM, Billard AB (2010) An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models. In: Proceeding of the international conference on robotics and automation, pp 2381–2388 Google Scholar
- 19.Khansari-Zadeh SM, Billard A (2010) Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming. In: Proceeding of the IEEE/RSJ international conference on intelligent robots and systems, pp 2676–2683 Google Scholar
- 21.Lemme A, Reinhart FR, Steil JJ (2010) Efficient online learning of a non-negative sparse autoencoder. In: European symposium on artificial neural networks, computational intelligence and machine learning Google Scholar
- 22.Li J, Jaeger H (2011) Minimal energy control of an ESN pattern generator. Technical report 26, Jacobs University Bremen Google Scholar
- 23.Li Z, Tsagarakis NG, Caldwell DG, Vanderborght B (2010) Trajectory generation of straightened knee walking for humanoid robot iCub. In: International conference control and automation, robotics and vision Google Scholar
- 24.Li Z, Vanderborght B, Tsagarakis NG, Caldwell DG (2010) Fast bipedal walk using large strides by modulating hip posture and toe-heel motion. In: IEEE international conference on robotics and biomimetics Google Scholar
- 25.Li Z, Vanderborght B, Tsagarakis NG, Caldwell DG (2010) Human-like walking with straightened knees, toe-off and heel-strike for the humanoid robot iCub. In: UKACC international conference on control Google Scholar
- 26.Maclellan MJ, Ivanenko YP, Cappellini G, Sylos LF, Lacquaniti F (2011) Features of hand-foot crawling behavior in human adults. J Neurophysiol Google Scholar
- 27.Martinez Salazar HR, Carbajal JP (2011) Including the passive dynamics of a compliant leg in the gait control. In: IEEE/RSJ international conference on intelligent robots and systems, San Francisco, CA, USA Google Scholar
- 28.Moro F, Tsagarakis N, Caldwell D (2011) A human-like walking for the compliant humanoid COMAN based on com trajectory reconstruction from kinematic motion primitives. In: 11th IEEE-RAS international conference on humanoid robots, Bled, Slovenia Google Scholar
- 29.Neumann G (2011) Variational inference for policy search in changing situations. In: Getoor L, Scheffer T (eds) Proceedings of the 28th international conference on machine learning. ACM, New York, pp 817–824 Google Scholar
- 30.Nordmann A, Emmerich C, Ruether S, Lemme A, Wrede S, Steil J (2012) Teaching nullspace constraints in physical human-robot interaction using reservoir computing. In: International conference on automation and robotics Google Scholar
- 32.Reinhart RF, Steil JJ (2011) Neural learning and dynamical selection of redundant solutions for inverse kinematic control. In: IEEE-RAS international conference on humanoid robots (humanoids), pp 564–569 Google Scholar
- 33.Rueckert E, Neumann G (2011) A study of morphological computation by using probabilistic inference for motor planning. In: 2nd international conference on morphological computation, Venice, Italy, pp 51–53 Google Scholar
- 34.Solopova I, Tihonova D, Grishin A, Ivanenko Y (2011) Assisted leg displacements and progressive loading by a tilt table combined with FES promote gait recovery in acute stroke. NeuroRehabilitation Google Scholar
- 35.Soltoggio A, Stanley KO (2012) From modulated hebbian plasticity to simple behavior learning through noise and weight saturation. Neural Network Journal (under review) Google Scholar
- 36.Sumioka H, Hauser H, Pfeifer R (2011) Computation with mechanically coupled springs for compliant robots. In: IEEE/RSJ international conference on intelligent robots and systems. IEEE Press, New York Google Scholar
- 37.Sylos LF, Ivanenko Y, Cappellini G, Gravano S, Lacquaniti F (2011) Smooth changes in the EMG patterns during gait transitions under body weight unloading. J Neurophysiol Google Scholar
- 38.Tsagarakis N, Zhiin L, Saglia J, Caldwell D (2011) The design of the lower body of the compliant humanoid robot cCub. In: International conference on robotics and automation, Shanghai Google Scholar
- 39.Sproewitz A, KuechlerL, Tuleu A, Ajallooeian M, D’Haene M, Moeckel R, Ijspeert AJ (2011) Oncilla robot—a light-weight bio-inspired quadruped robot for fast locomotion in rough terrain. In: Symposium on adaptive motion of animals and machines Google Scholar
- 40.Ugurlu B, Tsagarakis N, Spyrakos-Papastavridis E, Caldwell DG (2011) Compliant joint modification and real-time dynamic walking implementation on bipedal robot cCub. In: IEEE international conference on mechatronics Google Scholar
- 41.Wrede S, Johannfunke M, Lemme A, Nordmann A, Rüther S, Weirich A, Steil JJ (2010) Interactive learning of inverse kinematics with nullspace constraints using recurrent neural networks. In: 20. Workshop on computational intelligence. Fachausschuss Computational Intelligence der VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik, Dortmund Google Scholar
- 42.Wright W, Ivanenko Y, Gurfinkel V (2011) Foot anatomy specialization for postural sensation and control. J Neurophysiol Google Scholar