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update _qderiv_actuator_passive_vel with actuator force clamp case #1149

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update _qderiv_actuator_passive_vel with actuator force clamp case #1149

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14 changes: 14 additions & 0 deletions mujoco_warp/_src/derivative.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,11 +37,14 @@ def _qderiv_actuator_passive_vel(
actuator_biastype: wp.array(dtype=int),
actuator_actadr: wp.array(dtype=int),
actuator_actnum: wp.array(dtype=int),
actuator_forcelimited: wp.array(dtype=bool),
actuator_gainprm: wp.array2d(dtype=vec10f),
actuator_biasprm: wp.array2d(dtype=vec10f),
actuator_forcerange: wp.array2d(dtype=wp.vec2),
# Data in:
act_in: wp.array2d(dtype=float),
ctrl_in: wp.array2d(dtype=float),
actuator_force_in: wp.array2d(dtype=float),
# Out:
vel_out: wp.array2d(dtype=float),
):
Expand All @@ -64,6 +67,14 @@ def _qderiv_actuator_passive_vel(
vel_out[worldid, actid] = 0.0
return

# skip if force is clamped by forcerange
if actuator_forcelimited[actid]:
force = actuator_force_in[worldid, actid]
forcerange = actuator_forcerange[worldid % actuator_forcerange.shape[0], actid]
if force <= forcerange[0] or force >= forcerange[1]:
vel_out[worldid, actid] = 0.0
return

vel = float(bias)
if actuator_dyntype[actid] != DynType.NONE:
if gain != 0.0:
Expand Down Expand Up @@ -256,10 +267,13 @@ def deriv_smooth_vel(m: Model, d: Data, out: wp.array2d(dtype=float)):
m.actuator_biastype,
m.actuator_actadr,
m.actuator_actnum,
m.actuator_forcelimited,
m.actuator_gainprm,
m.actuator_biasprm,
m.actuator_forcerange,
d.act,
d.ctrl,
d.actuator_force,
],
outputs=[vel],
)
Expand Down
70 changes: 70 additions & 0 deletions mujoco_warp/_src/derivative_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,6 +209,76 @@ def test_step_tendon_serial_chain_no_nan(self):
self.assertFalse(np.any(np.isnan(mjd.qpos)))
self.assertFalse(np.any(np.isnan(mjd.qvel)))

def test_forcerange_clamped_derivative(self):
"""Implicit integration is more accurate than Euler with active forcerange clamping."""
xml = """
<mujoco>
<option timestep="0.01" integrator="implicitfast"/>
<worldbody>
<geom type="plane" size="10 10 0.001"/>
<body pos="0 0 1">
<joint name="slide" type="slide" axis="1 0 0"/>
<geom type="sphere" size="0.1" mass="1"/>
</body>
</worldbody>
<actuator>
<position joint="slide" kp="10000" kv="1000" forcerange="-10 10"/>
</actuator>
</mujoco>
"""

dt_small = 1e-4
dt_large = 1e-2
duration = 1.0
nsteps_large = int(duration / dt_large)
nsubstep = int(dt_large / dt_small)

# ground truth: Euler with small timestep
mjm_gt = mujoco.MjModel.from_xml_string(xml)
mjd_gt = mujoco.MjData(mjm_gt)
mjm_gt.opt.timestep = dt_small
mjm_gt.opt.integrator = mujoco.mjtIntegrator.mjINT_EULER
mujoco.mj_resetData(mjm_gt, mjd_gt)
mjd_gt.ctrl[0] = 0.5

# implicitfast at large timestep
mjm_impl, mjd_impl, m_impl, d_impl = test_data.fixture(xml=xml)
m_impl.opt.timestep.fill_(dt_large)
m_impl.opt.integrator = int(mujoco.mjtIntegrator.mjINT_IMPLICITFAST)
d_impl.ctrl.fill_(0.5)

# euler at large timestep
mjm_euler, mjd_euler, m_euler, d_euler = test_data.fixture(xml=xml)
m_euler.opt.timestep.fill_(dt_large)
m_euler.opt.integrator = int(mujoco.mjtIntegrator.mjINT_EULER)
d_euler.ctrl.fill_(0.5)

error_implicit = 0.0
error_euler = 0.0

for _ in range(nsteps_large):
# ground truth: small steps with Euler
mujoco.mj_step(mjm_gt, mjd_gt, nsubstep)

# implicit at large timestep
mjw.step(m_impl, d_impl)

# euler at large timestep
mjw.step(m_euler, d_euler)

# accumulate errors
gt_qpos = mjd_gt.qpos[0]
diff_implicit = gt_qpos - d_impl.qpos.numpy()[0, 0]
diff_euler = gt_qpos - d_euler.qpos.numpy()[0, 0]
error_implicit += diff_implicit * diff_implicit
error_euler += diff_euler * diff_euler

self.assertLess(
error_implicit,
error_euler,
"implicitfast should be more accurate than Euler at large timestep when forcerange derivatives are correctly handled",
)


if __name__ == "__main__":
wp.init()
Expand Down
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