3-DOF planar arm with Hill-type muscles
ADVANTAGES1 · dim 12SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
12D 3-DOF planar arm with 6 Hill-type muscles - joint angles, velocities, and muscle fiber dynamics
Problem definition
Millard et al. (2013); Zajac (1989); Haeufle et al. (2014)
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
Show canonical RHS excerpt
def _f_pe(l_ce, l_opt):
"""Parallel elastic element force (exponential toe region)."""
strain = (l_ce - l_opt) / max(l_opt, 1e-6)
if strain > 0:
return _K_PE * strain * strain
return 0.0
def _fl_active(l_ce, l_opt):
"""Active force-length relationship (Gaussian approximation)."""
x = (l_ce / max(l_opt, 1e-6) - 1.0) / _WIDTH
return max(1.0 - x * x, 0.0)
def musculoskeletal_arm_3dof_rhs(t, y):
q = y[0:3]
dq = y[3:6]
lce = np.maximum(y[6:12], 1e-4)
dy = np.empty(12)
# --- Muscle forces and CE dynamics ---
tau_muscle = np.zeros(_N_JOINTS)
for m in range(_N_MUSCLES):
fl = _fl_active(lce[m], _L_OPT[m])
# Tendon length (simplified: l_mt ~ l_slack + l_opt, constant path)
l_mt = _L_SLACK[m] + _L_OPT[m]
l_tendon = l_mt - lce[m]
k_tendon = 35.0 * _F_MAX[m] / max(_L_SLACK[m], 1e-6)
f_tendon = max(k_tendon * (l_tendon - _L_SLACK[m]), 0.0)
f_tendon += _DAMPING * (-dy[6 + m] if m > 0 else 0.0) # approx
# CE force at current v_ce (we solve for v_ce from force balance)
# F_CE + F_PE = F_tendon -> a*fl*fv*Fmax + f_pe = f_tendon
f_pe = _f_pe(lce[m], _L_OPT[m]) * _F_MAX[m]
target_ce_force = max(f_tendon - f_pe, 0.0)
# Invert Hill equation for v_ce:
# target = a * fl * fv(v) * Fmax
a_fl_fmax = _A_TONIC * fl * _F_MAX[m]
if a_fl_fmax > 1e-6:
ratio = target_ce_force / a_fl_fmax
# From fv = (1+v)/(1-v/0.25), solve for v:
# ratio*(1 - v/0.25) = 1 + v => v = (ratio - 1) / (ratio/0.25 + 1)
ratio = min(ratio, 1.39) # cap at lengthening limit
v_norm = (ratio - 1.0) / (ratio / 0.25 + 1.0)
v_ce = v_norm * _L_OPT[m] * _V_MAX
else:
# Very low activation: CE extends passively under tendon pull
v_ce = 0.01 * (f_tendon - f_pe) / max(_F_MAX[m], 1.0)
dy[6 + m] = v_ce
# Torque contribution
f_muscle = f_tendon
j = _JOINT_MAP[m]
tau_muscle[j] += _MOMENT[m] * f_muscle
# --- Joint dynamics: q'' = M^{-1} * (tau - C*q' - G) ---
# Diagonal inertia (simplified)
M_diag = _I_LINK.copy()
# Gravity torques
G_torque = np.empty(3)
for j in range(3):
G_torque[j] = -_M_LINK[j] * _G * _L_LINK[j] * np.sin(q[j])
# Viscous damping (simplified Coriolis/centrifugal substitute)
C_damp = 0.5 * dq
ddq = (tau_muscle - C_damp - G_torque) / M_diag
dy[0:3] = dq
dy[3:6] = ddq
return dy- Parameters
- _A_TONIC = 0.05
- _DAMPING = 0.1
- _F_MAX = [300, 250, 200, 300, 250, 200]
- _G = 9.81
- _I_LINK = [0.05, 0.03, 0.01]
- _JOINT_MAP = [0, 0, 1, 1, 2, 2]
- _K_PE = 5
- _L_LINK = [0.3, 0.25, 0.15]
- _L_OPT = [0.1, 0.1, 0.08, 0.08, 0.06, 0.06]
- _L_SLACK = [0.15, 0.15, 0.12, 0.12, 0.1, 0.1]
- _MOMENT = [0.04, -0.04, 0.03, -0.03, 0.02, -0.02]
- _M_LINK = [2, 1.5, 0.5]
- _N_JOINTS = 3
- _N_MUSCLES = 6
- _V_MAX = 10
- _WIDTH = 0.56
- Initial condition
- y(0) = [0.785, 1.571, 0, 0, 0, 0, 0.1, 0.1, 0.08, 0.08, 0.06, 0.06]
- Horizon
- t ∈ [0, 2]
Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: low
Default noise: none
Recommendation snapshot
Clean best: SciPy RK45
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
Versions & freeze
Methodology →- Freeze
- 2026-08-13
- libsolvsrk
- 2.3.0
- SciPy
- 1.14
- SUNDIALS
- CVODE (bundled backend)
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: 3-DOF planar arm with Hill-type muscles (3-dof-planar-arm-with-hill-type-muscles)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Results matrix
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SciPy RK45SciPy | 100% | - | 21,836 | 768 ms | 0.809 |
| 2 | SciPy DOP853SciPy | 100% | - | 109,166 | 3.77 s | 0.809 |
| 3 | SciPy RK23SciPy | 100% | - | 71,153 | 2.65 s | 0.809 |
| 4 | CVODE BDFexternal | 100% | - | 504 | 24 ms | 0.809 |
| 5 | CVODE Adamsexternal | 100% | - | 1,367 | 54 ms | 0.809 |
| 6 | Tsit5external | 100% | - | 4,188 | 1.23 s | 0.809 |
| 7 | Vern7external | 100% | - | 4,352 | 3.93 s | 0.809 |
| 8 | Vern9external | 100% | - | 7,202 | 4.01 s | 0.809 |
| 9 | TRBDF2external | 100% | - | 2,705 | 4.88 s | 0.809 |
| 10 | FBDFexternal | 100% | - | 1,014 | 4.73 s | 0.809 |
| 11 | SolvSRK | 100% | - | 5,055 | 155 ms | 0.809 |
| - | SciPy BDFSciPy | 0% | - | - | - | - |
| - | SciPy RadauSciPy | 0% | - | - | - | - |
| - | SciPy LSODASciPy | 0% | - | - | - | - |
At Clean, best balanced arm is SciPy RK45 · SolvSRK survival 100%.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
Cite this page
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is - a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_3_dof_planar_arm_with_hill_type_muscles_2026,
title = {Resonix Evidence Portal: 3-DOF planar arm with Hill-type muscles},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/3-dof-planar-arm-with-hill-type-muscles}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}Related
TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status