Looking for motor synergies in Darwin-OP biped robot

Abstract

In humans, the system that controls several biomechanical tasks has a modular organization (muscle synergies). While the muscle synergies in human walking were already studied deeply, this phenomenon in robotic system is not so explored and could bring simpler controllers requiring a minor number of parameters. So, the purpose of this work is to acquire and interpret synergies from selected joint signals in order to answer some specific research questions regarding the verification of synergistic control of a biped robot in a walking activity, e.g, the number of synergies, their activation profiles, their mechanical role, their quality to reproduce/control walking motions, etc. The selected robotic system was the Darwin-OP robot when walking both with a model based and a bio-inspired controller. Tests were performed with the simulated and real robot.The results appear to suggest that the extraction of synergies are quite useful for the robotic locomotion, since with only 2 synergies, is possible to reconstruct the signals of the studied joints. The biomechanical role for each determined synergy is described based on the functional transformation between sensorimotor signals and biomechanical output. Besides, the robustness of the determined synergies is verified in the locomotor activation pattern across strides and across speeds when the mechanical requirements do not change to a large extent.

Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task

“…The computational approach facilitates the analysis and evaluation of the role of each component using simple models that extract important factors of the actual system. For example, some studies have reported that motor synergy structures can be observed from trajectories acquired by machine learning and optimization: zero-moment point-based locomotion [ 12 ], feedback torque integration [ 13 ], optimal control [ 14 , 15 ], trajectory optimization [ 16 ], reinforcement learning for gait [ 17 ] and reaching [ 18 ]. These studies provide a methodology for approaching motor synergies without assuming how motor synergies are implemented in animals.…”

Biomimetic learning of hand gestures in a humanoid robot

“…Numerous investigations ( Tresch et al, 2006 ; Steele et al, 2013 ; Santuz et al, 2017 ; Taborri et al, 2017 ) have been done to show that synergies are not merely a mathematical representation but rather an efficient tool for comprehending how the CNS organizes motor control and coordination. As a result of such studies, promising results ( Artemiadis and Kyriakopoulos, 2006 ; Artemiadis et al, 2010 ; Hocaoglu and Patoglu, 2012 ; Cunha et al, 2016 ; Lunardini et al, 2016 ) have led to the use of synergies in several applications including robotics.…”

A small soft-bodied crawling robot with electromagnetic legs and neural control for locomotion on various metal terrains

“…It is computationally inexpensive due to the small neural circuit. The CPG acts as an internal clock, generating and modulating basic low-dimensional periodic signals, while the CPG postprocessing functions as a modular organization of motor patterns (known as motor primitives or synergies) [21], [22], converting the low-dimensional CPG signals into groups of high dimensional motor patterns for commanding all actuators to produce various crawling behaviors.…”