Variables | |
| a = np.random.rand(model.nv,1) | |
| data = model.createData() | |
| dtau_da = data.M | |
| dtau_dq = data.dtau_dq | |
| dtau_dv = data.dtau_dv | |
| lowerPositionLimit | |
| model = pin.buildSampleModelHumanoidRandom() | |
| In this short script, we show how to compute the derivatives of the inverse dynamics (RNEA), using the algorithms proposed in: More... | |
| q = pin.randomConfiguration(model) | |
| upperPositionLimit | |
| v = np.random.rand(model.nv,1) | |
| inverse-dynamics-derivatives.a = np.random.rand(model.nv,1) |
Definition at line 23 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.data = model.createData() |
Definition at line 14 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.dtau_da = data.M |
Definition at line 33 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.dtau_dq = data.dtau_dq |
Definition at line 31 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.dtau_dv = data.dtau_dv |
Definition at line 32 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.lowerPositionLimit |
Definition at line 18 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.model = pin.buildSampleModelHumanoidRandom() |
In this short script, we show how to compute the derivatives of the inverse dynamics (RNEA), using the algorithms proposed in:
Analytical Derivatives of Rigid Body Dynamics Algorithms, Justin Carpentier and Nicolas Mansard, Robotics: Science and Systems, 2018
Definition at line 13 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.q = pin.randomConfiguration(model) |
Definition at line 21 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.upperPositionLimit |
Definition at line 19 of file inverse-dynamics-derivatives.py.
| inverse-dynamics-derivatives.v = np.random.rand(model.nv,1) |
Definition at line 22 of file inverse-dynamics-derivatives.py.