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Optimize mj_tendonBias by computing Jdot * qvel directly

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PiperOrigin-RevId: 897628283
Change-Id: Iee26a95d6aaa379730b89be14746308742111e2d
This commit is contained in:
Yuval Tassa
2026-04-10 04:57:18 -07:00
committed by Copybara-Service
parent 025ba59fab
commit f114ea8038
3 changed files with 22 additions and 34 deletions
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test/engine/engine_core_smooth_test.cc
+5 -4
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@@ -191,10 +191,8 @@ TEST_F(CoreSmoothTest, TendonJdot) {
mj_forward(m, d);
// get current J and Jdot for the tendon
// get current J for the tendon
vector<mjtNum> ten_J(d->ten_J, d->ten_J + nv);
vector<mjtNum> ten_Jdot(nv, 0);
mj_tendonDot(m, d, 0, ten_Jdot.data());
// compute finite-differenced Jdot
mjtNum h = MjTol(1e-7, 5e-4);
@@ -206,7 +204,10 @@ TEST_F(CoreSmoothTest, TendonJdot) {
mju_subFrom(ten_Jh.data(), ten_J.data(), nv);
mju_scl(ten_Jh.data(), ten_Jh.data(), 1.0 / h, nv);
EXPECT_THAT(ten_Jdot, Pointwise(MjNear(1e-6, 2e-3), ten_Jh));
// test dot product against finite differences
mjtNum dot = mj_tendonDot(m, d, 0, d->qvel);
mjtNum expected_dot = mju_dot(ten_Jh.data(), d->qvel, nv);
EXPECT_NEAR(dot, expected_dot, MjTol(1e-5, 2e-3));
}
mj_deleteData(d);