Hi Johanna, thanks for the message. I'll add the relative paths in a future version!

Meanwhile, you can edit the XML file and point to the path in your computer to CyclingGeometry. Worst case scenario this is just a matter of visualisation and shouldn't matter in terms of simulations.

Hey Nick and Ross, these answers clarified a lot, thanks!

IPOPT perturbs the initial guess from the initial guess that's provided by the user by a small amount. Ah, that explains a lot. Thanks a lot, Ross! Do you know if there is a way to modify that? Also, do you have some idea what happens in a case where my guess is, for example, 4s, and I try to predi...

Hi Ross, thanks for the answer!

In the plots, I wrongly wrote rad/s; it is deg/s. My bad! The velocity is 1/2 cycle per second, or 1 cycle every 2s. So, this should be fine.

What do you mean by "bumping the solution"?

Hi everyone! I resumed the simulations recently and would like some extra help, if possible. I have the human model with 4 actuators on the joints (left and right hip and knees), and the feet are connected to the pedals of a crankset. With your help (thank you!), I've achieved the following mileston...

Hi Nick, that makes sense. I changed it back to control ranged from -1 to 1.

I am grateful for your assistance over the past few weeks. I'm confident to say that the issue I mentioned in this post has been successfully resolved. Thanks again! =]

Hey everyone! Just wanted to share a quick update on my simulation progress and give a big shout-out for your fantastic suggestions! I solved the problem of the post using the MocoStateTrackingGoal . First, I generate a sto file with time and angular speed data following a linear ramp pattern. def c...

Found my error! I forgot to set the control info, resulting in torques being limited to -1 to 1 instead of the desired -50 to 50 range: problem.setControlInfo('/tau_hip_flexion_r', [-joint_torque, joint_torque]) problem.setControlInfo('/tau_hip_flexion_l', [-joint_torque, joint_torque]) problem.setC...

Hi everyone, Thank you, Aaron, for sharing the paper. I've carefully reviewed it, and while the work is impressive, their prediction goals differ from mine. They utilise tracking data for foot and pedals, eliminating the need for kinematic constraints. In my case, I don't have markers, so I'm not fo...

Hey Nick, The model with the crank + human will apply forces to enforce the kinematic constraints, but these forces alone shouldn't lead to the crank motion. Yeah, I thought so as well, but it is weird. Are the joint torques much smaller than the crank resistance torque? I set the joint torques to [...