Pydrake离散时间仿真中双足机器人足地穿透问题的解决方案咨询

I’ve run into similar rigid contact stability issues with Drake when scaling up timesteps, so let’s walk through actionable fixes for your penalty-method-based simulation:

1. Increase Contact Penalty Stiffness #

The penalty method relies on stiffness to generate restoring forces when penetration occurs—and larger timesteps mean bigger potential displacements between steps. You haven’t mentioned setting the contact_penalty_stiffness parameter, which defaults to a relatively low value. Try cranking it up to a higher magnitude (start with 1e7 and adjust based on stability):

plant.set_contact_penalty_stiffness(1e7)

Note: Don’t go excessively high (e.g., 1e10), as this can introduce numerical instability into your simulation.

2. Refine Penetration Allowance & Stiction Tolerance Tuning #

While you can’t set penetration_allowance below your timestep (due to Drake’s constraints), you can tune the stiction tolerance to be proportional to your timestep. Try reducing it slightly to make the contact force activate more aggressively when small motions occur:

plant.set_stiction_tolerance(0.0005)  # Half your timestep

Additionally, ensure your penetration allowance is set to the minimum acceptable value for your timestep—you’re already doing this with 0.001, but double-check that it’s not larger than necessary (since looser allowances can enable more penetration).

3. Switch to an Implicit Integrator #

Drake’s default explicit Euler integrator struggles with rigid contact dynamics at larger timesteps because it’s conditionally stable. Implicit integrators (like Implicit Euler or Symplectic Euler) handle stiff systems much better. Replace your default integrator with this:

from pydrake.systems.analysis import ImplicitEulerIntegrator

# After finalizing your plant
plant.Finalize()
integrator = ImplicitEulerIntegrator(plant, dt=0.001)
simulator = Simulator(plant, integrator)

This will help the solver better resolve contact forces within each larger timestep, reducing penetration.

4. Fix Your Coulomb Friction Parameter #

You have a typo in your ground friction setup—you’re setting static_friction twice instead of defining both static and dynamic friction. Dynamic friction affects sliding behavior, which can indirectly impact contact stability:

ground_friction = CoulombFriction(static_friction=1.0, dynamic_friction=0.9)

Using a realistic dynamic friction value (slightly lower than static) will make contact forces more physically consistent.

5. Add Contact Constraints to Inverse Dynamics #

If your inverse dynamics optimization doesn’t explicitly enforce non-penetration constraints, consider adding them. For example, you can include a cost term that penalizes foot positions below the ground plane, or add hard constraints to keep the foot’s z-position above the ground:

# Example: Add a position constraint to prevent foot penetration
from pydrake.multibody.inverse_kinematics import AddPositionConstraint
import numpy as np

# Get the foot body frame (replace with your actual foot frame name)
foot_frame = plant.GetFrameByName("FootFrame")
# Constrain foot z-position to be >= 0 (ground plane)
AddPositionConstraint(
    plant=plant,
    frameB=foot_frame,
    p_BQ=[0, 0, 0],
    frameA=plant.world_frame(),
    p_AQ_lower=[-np.inf, -np.inf, 0.0],
    p_AQ_upper=[np.inf, np.inf, np.inf]
)

This gives your inverse dynamics solver an explicit signal to avoid penetration.

Start with steps 1, 3, and 4 first—these are the quickest wins for resolving penetration at 0.001s timesteps. If you still see issues, move on to tuning the tolerance parameters and adding constraints.

内容的提问来源于stack exchange，提问作者Zhenyuan Fu