- Download the movie (32M) which shows the general idea of this project.
- Download this master project.
When we look at the animal kingdom, we can agree our robotic technology is far from being able to cope with unpredictable environments as animals do. Animals manage to move in any environment with an astonishing fuidity. What are the key elements which allow a biological system to move in a given environment? The answer to this question is unfortunately far from being complete, and we will bring below only a little piece of the whole puzzle.
Early creatures which evolved limbs to cope with a terrestrial environment, rather than an aquatic one, needed a stable controller able to deal with unpredictable terrains. Such controllers for legged locomotion seem very complex indeed, since it is easier to make a wheel roll than a legged machine walk. And if natural wheels does not emerge from evolution, how can a controller for legs emerge despite its supposed high complexity?
Maybe the complexity operates at another level, like on the intrinsic properties of the legs. Could legs have intrinsic properties which help them to be stable? It is obvious the properties of materials used to build an arti
About this master project
In the present master project, we will reproduce this walking model with a small modi
In this experiment, we are interested to see if we can obtain a stable walking gait by generating random feedback parameter sets.
We can obtain 1 good parameter set every 375 trials with a probability of 95%. Below are shown different parameter sets which give different gaits and are stable (they all do not look natural):
Parameter set 1: download movie
Parameter set 2: download movie
Parameter set 7: download movie
Parameter set 8: download movie
Parameter set 12: download movie
Experiment 2, 3, 4, 5, 6
Here, we optimize the feedback parameter sets with two different algorithms:
- Home made optimization algorithm
Starting from the initial parameter sets obtained in experiment 1, we optimize the energy consumption in muscles. We observe the final gaits are smoother and look more natural than in experiment 1, however the algorithm is slow.
Experiment 2, parameter set 8: download movie
Experiment 3, parameter set 6: download movie
- PSO algorithm
Using the particle swarm optimization algorithm, we optimize again the energy consumption in muscles. This algorithm is faster than the previous one. In experiment 4 and 5, we observe the algorithm efficiently minimize the energy consumption of the walking gait, however the final results lack stability in experiment 4 and do not look natural in experiment 5.
Therefore, in experiment 6 we add a signal dependant noise (SDN) in the muscle activation. This final experiment shows that energy consumption optimization coupled with SDN converge to a smoother and more natural gait than in the previous experiments.
Experiment 4, parameter set 3: download movie
Experiment 5, parameter set 1: download movie
Experiment 5, parameter set 3: download movie
Experiment 6, parameter set 4: download movie
Stumbling corrective response
In this experiment, we implemented the stumbling corrective response. The humanoid avoid falling in 95% of the case its foot hit the obstacle (10 cm height) between 50% to 77% of the stride.
In the control experiment, we show the humanoid fall if no SCR reflexe are implemented. The figures below show exemples of early and mid stance SCR.
Control experiment: without SCR reflexe: download movie
SCR in early swing: download movie
SCR in mid-swing: download movie
Bibliography D.F.B.Haeue, S.Grimmer, A.Seyfarth, The role of intrinsic muscle properties for stable hopping – stability is achieved by the force velocity relation, Bioinspiration and Biomimetics, vol.5, 2010  H.Geyer, A.Seyfarth, R.Blickhan, Positive force feedback in bouncing gaits?, The Royal Society, 270, pp.2173-2183, 2003  H.Geyer, H.Herr, A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities, IEEE transaction on neural systems and rehabilitation engineering, vol.18,N°3, pp263-273, 2010  F.C. Anderson, M.G.Pandy, Dynamic optimization of human walking, Journal of biomechanical engineering, Vol 123, pp. 381-390, 2001.  Kelvin E. Jones, Antonia D. de C. Hamilton, Daniel M. Wolpert, Sources of signal-dependent noise during isometric force production, Journal of neurophysiology, 88, pp 1533-1544, 2002.