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import math |
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import torch |
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import pytorch_kinematics as pk |
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def test_correctness(): |
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chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7") |
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th = torch.tensor([0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0]) |
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J = chain.jacobian(th) |
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assert torch.allclose(J, torch.tensor([[[0, 1.41421356e-02, 0, 2.82842712e-01, 0, 0, 0], |
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[-6.60827561e-01, 0, -4.57275649e-01, 0, 5.72756493e-02, 0, 0], |
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[0, 6.60827561e-01, 0, -3.63842712e-01, 0, 8.10000000e-02, 0], |
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[0, 0, -7.07106781e-01, 0, -7.07106781e-01, 0, -1], |
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[0, 1, 0, -1, 0, 1, 0], |
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[1, 0, 7.07106781e-01, 0, -7.07106781e-01, 0, 0]]])) |
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chain = pk.build_chain_from_sdf(open("simple_arm.sdf").read()) |
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chain = pk.SerialChain(chain, "arm_wrist_roll_frame") |
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th = torch.tensor([0.8, 0.2, -0.5, -0.3]) |
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J = chain.jacobian(th) |
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torch.allclose(J, torch.tensor([[[0., -1.51017878, -0.46280904, 0.], |
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[0., 0.37144033, 0.29716627, 0.], |
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[0., 0., 0., 0.], |
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[0., 0., 0., 0.], |
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[0., 0., 0., 0.], |
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[0., 1., 1., 1.]]])) |
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def test_jacobian_at_different_loc_than_ee(): |
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chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7") |
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th = torch.tensor([0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0]) |
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loc = torch.tensor([0.1, 0, 0]) |
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J = chain.jacobian(th, locations=loc) |
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J_c1 = torch.tensor([[[-0., 0.11414214, -0., 0.18284271, 0., 0.1, 0.], |
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[-0.66082756, -0., -0.38656497, -0., 0.12798633, -0., 0.1], |
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[-0., 0.66082756, -0., -0.36384271, 0., 0.081, -0.], |
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[-0., -0., -0.70710678, -0., -0.70710678, 0., -1.], |
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[0., 1., 0., -1., 0., 1., 0.], |
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[1., 0., 0.70710678, 0., -0.70710678, -0., 0.]]]) |
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assert torch.allclose(J, J_c1) |
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loc = torch.tensor([-0.1, 0.05, 0]) |
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J = chain.jacobian(th, locations=loc) |
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J_c2 = torch.tensor([[[-0.05, -0.08585786, -0.03535534, 0.38284271, 0.03535534, -0.1, -0.], |
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[-0.66082756, -0., -0.52798633, -0., -0.01343503, 0., -0.1], |
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[-0., 0.66082756, -0.03535534, -0.36384271, -0.03535534, 0.081, -0.05], |
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[-0., -0., -0.70710678, -0., -0.70710678, 0., -1.], |
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[0., 1., 0., -1., 0., 1., 0.], |
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[1., 0., 0.70710678, 0., -0.70710678, -0., 0.]]]) |
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assert torch.allclose(J, J_c2) |
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th = th.repeat(2, 1) |
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loc = torch.tensor([[0.1, 0, 0], [-0.1, 0.05, 0]]) |
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J = chain.jacobian(th, locations=loc) |
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assert torch.allclose(J, torch.cat((J_c1, J_c2))) |
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def test_parallel(): |
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N = 100 |
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chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7") |
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th = torch.cat( |
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(torch.tensor([[0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0]]), torch.rand(N, 7))) |
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J = chain.jacobian(th) |
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for i in range(N): |
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J_i = chain.jacobian(th[i]) |
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assert torch.allclose(J[i], J_i) |
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def test_dtype_device(): |
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N = 1000 |
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d = "cuda" if torch.cuda.is_available() else "cpu" |
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dtype = torch.float64 |
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chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7") |
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chain = chain.to(dtype=dtype, device=d) |
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th = torch.rand(N, 7, dtype=dtype, device=d) |
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J = chain.jacobian(th) |
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assert J.dtype is dtype |
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def test_gradient(): |
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N = 10 |
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d = "cuda" if torch.cuda.is_available() else "cpu" |
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dtype = torch.float64 |
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chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7") |
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chain = chain.to(dtype=dtype, device=d) |
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th = torch.rand(N, 7, dtype=dtype, device=d, requires_grad=True) |
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J = chain.jacobian(th) |
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assert th.grad is None |
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J.norm().backward() |
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assert th.grad is not None |
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def test_jacobian_prismatic(): |
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chain = pk.build_serial_chain_from_urdf(open("prismatic_robot.urdf").read(), "link4") |
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th = torch.zeros(3) |
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tg = chain.forward_kinematics(th) |
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m = tg.get_matrix() |
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pos = m[0, :3, 3] |
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assert torch.allclose(pos, torch.tensor([0, 0, 1.])) |
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th = torch.tensor([0, 0.1, 0]) |
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tg = chain.forward_kinematics(th) |
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m = tg.get_matrix() |
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pos = m[0, :3, 3] |
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assert torch.allclose(pos, torch.tensor([0, -0.1, 1.])) |
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th = torch.tensor([0.1, 0.1, 0]) |
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tg = chain.forward_kinematics(th) |
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m = tg.get_matrix() |
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pos = m[0, :3, 3] |
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assert torch.allclose(pos, torch.tensor([0, -0.1, 1.1])) |
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th = torch.tensor([0.1, 0.1, 0.1]) |
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tg = chain.forward_kinematics(th) |
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m = tg.get_matrix() |
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pos = m[0, :3, 3] |
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assert torch.allclose(pos, torch.tensor([0.1, -0.1, 1.1])) |
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J = chain.jacobian(th) |
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assert torch.allclose(J, torch.tensor([[[0., 0., 1.], |
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[0., -1., 0.], |
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[1., 0., 0.], |
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[0., 0., 0.], |
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[0., 0., 0.], |
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[0., 0., 0.]]])) |
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if __name__ == "__main__": |
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test_correctness() |
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test_parallel() |
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test_dtype_device() |
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test_gradient() |
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test_jacobian_prismatic() |
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test_jacobian_at_different_loc_than_ee() |
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