Morphological Properties of Mass-Spring-Damper Networks for Optimal Locomotion Learning

Morphological Properties of Mass-Spring-Damper Networks for Optimal Locomotion Learning

Robotic Embodiment

The combination of brain inspired AI and robotics is in the core of our work in the Human Brain Project. AI is a vague concept that originated from computer sciences many decades ago and encompasses all algorithms that mimic some cognitive functions of the human species. They are increaslingly based on methods that learn automatically from big datasets.

However, applying those methods to control robots is not as straightforward as it could seem. Unlike computer software, robots generally evolve in noisy and continuously changing environments but on the other hand, their mechanical complexity can be seen as an asset to simplify the control. This is studied through the fields of embodiment and morphological computation. Extreme examples have shown that mechanical structures could provide very natural behavior with no controller at all.

The Passive Walker experiment from T. McGeer is a powerful demonstration emphazing the importance of the body design versus the controller complexity to obtain robust and natural locomotion gaits.

Towards a Formalization of the Concept

Some recent investigations have tried to formalize the relation between the dynamical complexity of a mechanical system and its capability to require simple control. To this goal, a simple yet efficient tool consists in simulating structures composed of masses connected with actuated damper-spring links.

To extend this research, we developed a basic simulator of mass-spring-damper (MSD) networks and optimized a naive locomotion controller to provide them with efficient gaits in term of traveled distance and dissipated power. Three experiments have been done in open-loop to determinate the influence of the size of a structure (estimated though the number of nodes), the compliance (inverse of the spring stiffness) and the saturation at high powers.

This video presents several simulation renditions. The different locomotion processes displayed are learned through optimization in open-loop control.

In the second part of this work, the capacity of realizing closed-loop control in a very simple way requiring very few numerical computations has then been demonstrated.

clThe principal components in the closed-loop learning pipeline consist in a readout layer which is trained at each time step and a signal mixer that gradually integrates the feedback in the actuation signal.

Our Contribution

A full discussion about the results is accessible directly in this article under Creative Common license.

This work has been realized at Ghent Uuniversity together with Jonas Degrave, Francis wyffels, Joni Dambre and Benonie Carette. It is mainly mainly academic and provides a methodology to optimize a controller for locomotion and indications on what we can expect from its complexity to be able to realize this experiment. In the future, this knowledge will be used to conduct similar experiments on quadruped robots both in the real world and in simulation using the Neuro-Robotic Platform (NRP) developed in HBP.

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s