Home Research Project Details D1 - Biomechanics and adaptive neural control of animal and robot locomotion
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D1 - Biomechanics and adaptive neural control of animal and robot locomotion

Poramate Manoonpong, Florentin Wörgötter, and Ulrich Parlitz

How to generate smooth biological motion by the interplay of biomechanics and neuronal control is little understood. Successful bio-mimetic models have been implemented by us [Manoonpong et al. 2007, Steingrube et al. 2010], but remain limited using specific network design paired with targeted – and, thus, somewhat restricted – plasticity. Goal of this project is to achieve more flexible, transferable solutions and a better understanding of the general control & plasticity principles in neural motor function. For this we will combine novel biomechanical principles with improved learning mechanisms in the control networks. Specifically we will for the first time combine three important aspects: synaptic plasticity, forward control (efferent copies [von Holst et al. 1950]), and predictive motor models (motor planning [Matheson 2008, Kawato 1999]). Preliminary results on combining learning with forward models exist [Schröder-Schetelig et al. 2010]. Such a three-fold combination will allow us to create a system that can react proactively (in an anticipatory manner) to upcoming events, like a terrain change. Our main substrates are R1: two- and R2: six-legged walking robots. Specifically we will perform the following steps:

i) Improve the biomechanics of our walking robots (R1 and R2) by integrating elastic and passive joint properties to imitate muscle properties for better self-stabilization through mechanical feedback, called “preflexes” [Full et al. 1999]. Only the combination of appropriate biomechanics with advanced control will assure robust and smooth locomotion of the robots.

ii) Develop improved adaptive neural mechanisms for R1 and R2:
(1) Learn internal models from efference copies (motor signals) for self-adaptation, and disturbance anticipation [Schröder-Schetelig et al. 2010] using near (on-leg sensors) and far (camera) sensor systems.
(2) Implement neural motor memory to enhance learning capability through experience and long-term memorization of walking relevant events (walking event memory).
(3) Implement neural models of motor planning based on functions realized partly in the posterior parietal cortex [for “reaching” see [Gail et al. 2006] and cooperation with D2] and motor program disinhibition similar to the function of the basal ganglia.

iii) These advanced methods will be compared to the demands in orthosis and prosthesis with Otto Bock Healthcare addressing the difficult question of sensor driven walking event recognition and the thereby arising prospective motor planning also for prosthetic devices.

Building on large prior expertise in this field [Manoonpong et al. 2007, Steingrube et al. 2010, Schröder-Schetelig et al. 2010], this study will be the first to create a walking system based on neural control that reacts in an anticipatory way, that can correct errors during walking, and remember the context in which an error occurred. These properties would be vital for advanced prosthetic and orthopaedic devices helping patients in a pro-active way.



Fig. 1: Left, Developed walking robots. Middle: Basic modular neural control for locomotion of the robots where adaptive neural mechanisms can be integrated [Manoonpong et al. 2010]. Right: E-MAG Active orthosis and C-leg by Otto Bock Healthcare.

Belongs to Group(s):
Complex Dynamical Systems, Computational Neuroscience

Is part of  Section D 

Members working within this Project:
Wörgötter, Florentin 
Parlitz, Ulrich 
Manoonpong, Poramate 

Selected Publication(s):

Ren, G, Chen, W, Dasgupta, S, Kolodziejski, C, Wörgötter, F, and Manoonpong, P (2015).
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
Information Sciences 294:666-682.

Barikhan, SS, Wörgötter, F, and Manoonpong, P (2014).
Multiple Decoupled CPGs with Local Sensory Feedback for Adaptive Locomotion Behaviors of Bio-inspired Walking Robots
From Animals to Animats 13, ©2014 Springer International Publishing Switzerland (Lecture Notes in Computer Science edition)(ISBN: 978-3-319-08863-1 978-3-319-08864-8).

Braun, J, Wörgötter, F, and Manoonpong, P (2014).
Internal models support specific gaits in orthotic devices
Konferenzband: Mobile Service Robotics:539-546.

Chatterjee, S, Nachstedt, T, Wörgötter, F, Tamosiunaite, M, Manoonpong, P, Enomoto, Y, Arizumi, R, and Matsuno, F (2014).
Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units
Konferenzband: 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD):1-7.

Dasgupta, S, Wörgötter, F, and Manoonpong, P (2014).
Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control
Frontiers in Neural Circuits 8(Article 126):1-21.

Goldschmidt, D, Wörgötter, F, and Manoonpong, P (2014).
Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots
Frontiers in Neurobotics 8(Article 3):1-16.

Kuhlemann, I, Braun, J, Wörgötter, F, and Manoonpong, P (2014).
Comparing arc-shaped feet and rigid ankles with flat feet and compliant ankles for a dynamic walker
Konferenzband: Mobile Service Robotics:353-360.

Manoonpong, P, Dasgupta, S, Goldschmidt, D, and Wörgötter, F (2014).
Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot
Konferenzband: 2014 International Joint Conference on Neural Networks (IJCNN):3295-3302.

Manoonpong, P, Wörgötter, F, and Laksanacharoen, P (2014).
Biologically inspired modular neural control for a leg-wheel hybrid robot
Advanced in Robotics Research 1(1):101-126.

Xiong, X, Wörgötter, F, and Manoonpong, P (2014).
Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification
Robotics and Autonomous Systems 62(12):1777-1789.

Xiong, X, Wörgötter, F, and Manoonpong, P (2014).
Virtual agonist-antagonist mechanisms produce biological muscle-like functions An application for robot joint control
Industrial Robot-an International Journal 41(4):340-346.

Zeidan, B, Dasgupta, S, Wörgötter, F, and Manoonpong, P (2014).
Adaptive Landmark-Based Navigation System Using Learning Techniques
From Animals to Animats 13, ©2014 Springer International Publishing Switzerland (From Animals to Animats 13 Lecture Notes in Computer Science edition)(ISBN: 8575).

Dasgupta, S, Wörgötter, F, and Manoonpong, P (2013).
Information dynamics based self-adaptive reservoir for delay temporal memory tasks
Evolving Systems 4(4):235-249.

Manoonpong, P, Kolodziejski, C, Wörgötter, F, and Morimoto, J (2013).
COMBINING CORRELATION-BASED AND REWARD-BASED LEARNING IN NEURAL CONTROL FOR POLICY IMPROVEMENT
Advances in Complex Systems 16(Issue 02n03):1350015 (38 pp).

Manoonpong, P, Parlitz, U, and Wörgötter, F (2013).
Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines
Frontiers in Neural Circuits 7(Article 12):1-28.

Parlitz, U (2012).
Detecting generalized synchronization
Nonlinear Theory and Its Applications (NOLTA) IEICE 3(2):113-127.

Xiong, X, Wörgötter, F, and Manoonpong, P (2012).
An Adaptive Neuromechanical Model for Muscle Impedance Modulations of Legged Robots
Dynamic Walking 2012 (DWC2012), May 21-25, 2012 Florida, United States.

Tetzlaff, C, Kolodziejski, C, Timme, M, Manoonpong, P, and Wörgötter, F (2011).
Synaptic Scaling Generically Stabilizes Circuit Connectivity
BMC Neuroscience 2011 12(1):372.

Yu, D, and Parlitz, U (2011).
Inferring Network Connectivity by Delayed Feedback Control
PLoS ONE 6(9):e24333.

Yu, D, and Parlitz, U (2010).
Inferring local dynamics and connectivity of spatially extended systems with long-range links based on steady-state stabilization
Phy. Rev. E 82:026108.