yes, but that 's because the model does not has the correct gait path. with eth sin wave, only one leg is touching the ground plane at any given time, so it is spending mor the energy in falling. but making 180 offset we get two, but now is just going up and down. that idea is that is always has three contact to support the body at all times. That's for a walk or for a stance.
do no worry we will get there tonight I believe.
this is actually quite a good test, because it shows the robustness of the system.
whet come next is a state machine that on each state we model a controller type.
for example the walk, could be procedural, (that the one I will made first)
but we can make adaptation that use an inverted pendulum for predict the position of the foot, in fact I can see easily that the walk cycle be made but just the kinematic motion of an inverted pendulum fitted to each leg.
another way to go is that we can train a walking robot using Reinforcement learning, it is my contention that if we use a Deep learning reinforment learning on system like this, the training time should be reduced by more than one order of magnitude.
The reason I say this is because on this system the input space is far less noisy.
In all the system I seem, the deep leaning neral net spend most the time tunning joint toque, which is extremally noisy. plus they also has to make up for the bad physics engines, when the ai had to secund guess the physics.
In this system the imput are the three angles of the effector for each foot. really three, since is reduce to a position on a plane and a swivel angle, (but the machine can figure that)
and the output is the balance and the maximization for the Goal.
I am sure you have seeing some of the funny animation generated by tensor flow and OpenAI, they are touted as great success and I see them quite mediocre.
It is my contention that if the physics model is obeying the laws of physics as much as possible, that the outcome should be close to what nature produces. rather that this *.
https://www.youtube.com/watch?v=gn4nRCC9TwQ&t=45shttps://www.youtube.com/watch?v=2cjkKnAxCugon that training that AI exploited a severe flaw of the physics engine, which is that the engine does not conserve momentum, so if he throws the arm forward, there rest of the body react very little because the momentum reactions vanish on the solver soft joints. so the reinforment reward policy gives a reward for that action every time, so even in a long or short sequence it always gets that reword con conclude that those are legal valid motions.
I expect a far, far better behavior than that.