Author: Gabriel Urbain (IDLab, UGent)

A quadruped robot with traditional computation hardware as a step for a SpiNNaker version

In this post, we describe the components and the architecture of the Tigrillo robot, a compliant quadruped platform controlled with a Raspberry Pi to achieve early research on CPGs and transfer learning. In order to situate the technical description that follows in a scientific context, it may be useful to explain the research methodology that is used:

  1. Optimisation of a parametric CPG controller using the NRP and the VirtualCoach
  2.  Transfer and validation on the Tigrillo quadruped platform
  3. Collection and analysis of sensors feedback ont the robot and in the NRP to design and improve a robust closed-loop system
  4.  Implementation of the CPGs using NEST on the NRP
  5. Transfer and validation on Oncelot, our quadruped robot embedding SpiNNaker hardware
  6. Comparaison between simulations and the real platforms and extraction of knowledge to iterate on step 1.

The Tigrillo robot enables step 2 by providing a robot to validate the accuracy an general behavior in the NRP simulations.

Mechanical details:

The design process of Tigrillo platform have been guided considering three main features for the robot: compliance, cheapness, versatility. The compliance is a key element in this research as it is believed to add efficiency and robustness to locomotion, like what we can see in biology. However, it also challenges classical control techniques as the dynamics of the robot is now governed by equations with a higher complexity level. On the current platform, the compliance is mainly ensure by using springs in the legs knee instead of actuating them.

Electrical and Software architecture:

  • Sensors and Actuators: 4 Dynamixel RX-24F servomotors, an IMU (Inertial Measurement Unit), various force and flexion sensors in the feet and legs
  • Power supply: A DC step-up voltage convertor connected to a 3 cells LiPo battery to supply the boards and motors with a regulated voltage and a stalk current that can rise to 10A when the legs are pushing together and the motors have to deliver a high torque.
  • Control Board: A OpenCM board (based on an Atmel ARM Cortex-M3 microprocessor) that reads the analog sensor values at a constant frequency and send the position or velocity commands to the servomotors using the protocol standard defined by Dynamixel.
  • Computation board: A Raspberry Pi with Ubuntu Mate 16.04 that implements a CPG controller included  in the same Python software stack that the one used in the NRP and thus easily switch from simulation to trials and validation in the  real world.

tigrillo_electrical_schema

The software repository also includes board documentation on the top of the python code used for control and simulation.

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A one-day workshop during the last Performance Show in Ghent

Last week, we had the chance to organize the first edition of a SP10 Performance Show in the city of Ghent, Belgium. This two-days meeting between all the partners involved in the HBP Neurorobotics subproject (SP10) was an opportunity to discuss the latest progress of each research groups and ensure a convergence of views and efforts for the next events, researches and developments.

 

SP10 Performance Show September 2017
A discussion during the SP10 Performance Show

 

On the second day, we divided our work into two tracks. Whereas the Main Track dealt with administrative and research activities, the Secondary Track was organized as a workshop on the theme Thinking the NRP of the Future. It was formatted as short one-day hackaton where everyone started by summarizing one or several iconic research advances that had been done in the last year in his field, which helped us grouping into 4 different work teams :

  • Reinforcement Learning with the NRP
  • Integrating worms brains and soft bodies in the NRP
  • Real-time interaction between real and simulated robots in the NRP
  • Helping research on visuomotor learning with the child using simulations in the NRP

 

SP10 Performance Show September 2017
On Tuesday, a work group is brainstorming about integrating worms in the NRP

 

Each of those teams brainstormed to imagine and design an experiment that could help research to move forward and a list of requirements in term of developments it would need to be achieved. After lunch, the results of this brainstorm were presented to everyone to get feedback and comments before we started working on designing a first prototype in the NRP and coding some useful models that we would need in further work. To be continued then…

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.