ugently need help on a very simple conceptual problem

Hi there,

I am trying to understand why I fail to produce identical qdd when I use the same robot to perform forward dynamics twice.

Here is the test script:

``

n_data=100
device="cpu"
gt_robot_model = DifferentiableKUKAiiwa(device=device)
train_data = generate_sine_motion_forward_dynamics_data(
    gt_robot_model, n_data=n_data, dt=1.0 / 250.0, freq=0.1
)
train_loader = DataLoader(dataset=train_data, batch_size=100, shuffle=False)

for g in [True, False]:
    for d in [True, False]:
        
        for batch_idx, batch_data in enumerate(train_loader):
                q, qd, qdd, tau = batch_data
                
        
                qdd_pred = gt_robot_model.compute_forward_dynamics(
                    q=q, qd=qd, f=tau, include_gravity=g, use_damping=d
                )
        
                print (g, d, torch.sum(torch.abs(qdd_pred - qdd)))

printout: True True tensor(15084.983) True False tensor(19104.133) False True tensor(20023.293) False False tensor(23508.631)

Please let me know which concepts I have failed to grasp, so that I can read up on them. Many thanks,

[fmeier]

Hi,

ah yes, now I understand. I was able to reproduce the issue on my side. It seems like there is a bug in our new compute_forward_dynamics function (batch_size=1 works, but batch_size > 1 does something weird). @exhaustin

While we’re debugging this, you can use compute_forward_dynamics_old , which is conceptually the same and works for any batch_size, but is slower. I just confirmed that on my side using compute_forward_dynamics_old you get a near 0 difference when both gravity and damping is set to True.

Hope this helps, will notify you when we fixed the newer and faster forward dynamics computation!

[WilsonFok2020]

Thank you very much.