pinocchio  2.6.3
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
4) Snap your fingers (aka direct and inverse dynamics)


The main objective of the tutorial is to implement a simple torque control inside a home-made contact simulator.

4.0) Technical prerequisites


We are going to use a 4-finger hand, whose model is defined in Python (no urdf model) using capsule volumes. The code of the robot is available here. It needs a Display class wrapping the Gepetto-viewer client available here, and contains a Robot class implementing the robot hand and a simple example si available here.

from robot_hand import Robot
robot = Robot()

Take care that the hand is small: zoom in to see it in the window (or press the space bar).


We will need a proper QP solver with inequality. QuadProg is a Python wrap of a nice Golub-based solver. Install it with PIP

pip install --user quadprog

QuadProg main function is solve_qp. You have a bit of documentation using the Python help command help(solve_qp). A simple example follows:

from quadprog import solve_qp
# Solve min_x .5 xHx - gx s.t. Cx <= d
x, _, _, _, _, _ = solve_qp(H, g, C, d)

4.1. Direct dynamics

Choosing an arbitrary joint torque \(\tau_q\), we now compute the robot acceleration and integrate it.

The dynamic equation of the robot is \(M a_q + b = \tau_q\), with \(M\) the mass, \(a_q\) the joint acceleration and \(b\) the drift. The mass matrix can be computed using CRB algorithm (function of \(q\)). The drift is computed using RNE algorithm (function of \(q\), \(v_q\) and \(a_q\) with \(a_q=0\)).

import pinocchio as se3
q = rand(robot.model.nq)
vq = rand(robot.model.nv)
aq0 = zero(robot.model.nv)
# compute dynamic drift -- Coriolis, centrifugal, gravity
b = se3.rnea(robot.model,, q, vq, aq0)
# compute mass matrix M
M = se3.crba(robot.model,, q)

These terms correspond to the inverse dynamics. They can be numerically inverted to compute the direct dynamics.

Question 1

Using \(M\) and \(b\) computed by the above algorithms, and knowing a given set of joint torques \(\tau_q\), compute \(a_q\) so that \(M*a_q+b = \tau_q\).

Once \(a_q\) as been computed, it is straight forward to integrate it to velocity using \(v_q += a_q * dt\). Integration to joint position is more complex in general. It is implemented in pinocchio:

q = se3.integrate(robot.model, q, vq * dt)

In the particular case of only simple joints (like the robot hand), the same integration \(q += v_q * dt\) also holds.

Question 2

Implement the simulation of the robot hand moving freely with constant (possibly 0) torques. Implement a variation where the torques are only joint friction ( \(\tau_q = -K_f v_q\) at each iteration).

4.2) PD and computed torques

Now choose a reference joint position (possibly time varying, like in the hand example). The joint torques can then be computed to track the desired position, with \(\tau_q = -K_p (q-q_{des}) - K_v v_q\). Both gains \(K_p\) and \(K_v\) should be properly chosen. Optimal tracking is obtained with \(K_v = 2 \sqrt{K_p}\). In general, a desired velocity is also tracked to avoid tracking errors.

Question 3

Implement then simulate a PD, by compute the torques from a PD law, then integrate it using the simulator of question 2.

Here, there is a strong coupling between joints, due to the mass matrix that is not compensated in the simple PD law. In theory, the computed torques is to compute the joint torque by inverse dynamics from a reference joint acceleration. With boils down to canceling the simulation equation by choosing the proper terms in the control law. It is now very interesting to implement in case of perfect dynamics knowledge. It might be more interesting to study in case the simulation is done with the perfect M, while the control is computed with approximate M (for example, using only the diagonal terms of the mass matrix). Let's rather simulate contact.

4.3) Collision checking

The robot hand is composed of capsules, i.e. level-set of constant distance to a segment. Collision checking and distances are then easy to implement. The source code of collision checking is available in the file. Pinocchio also implement a complete and efficient collision checking based on FCL, also not used in the tutorial.

Collision checking are done for a set of collision pairs that must be specified to the robot. The collision checking method indeed compute the distance between the two objects, along with the so-called witness points. A method can also be used to display them.

from robot_hand import Robot
robot = Robot()
# Create 10 witness points in the rendering window
for i in range(10):
robot.viewer.viewer.gui.addCylinder('world/wa%i' % i, .01, .003, [1, 0, 0, 1])
robot.viewer.viewer.gui.addCylinder('world/wb%i' % i, .01, .003, [1, 0, 0, 1])
robot.viewer.viewer.gui.setVisibility('world/wa%i' % i, 'OFF')
robot.viewer.viewer.gui.setVisibility('world/wb%i' % i, 'OFF')
# Add 4 pairs between finger tips and palm
robot.collisionPairs.append([2, 8])
robot.collisionPairs.append([2, 11])
robot.collisionPairs.append([2, 14])
robot.collisionPairs.append([2, 16])
# Compute distance between object 2 and 8, i.e the first collision pair
idx = 0
dist = robot.checkCollision(idx)
# Display the collision pair by adding two disks at the witness points.
robot.displayCollision(idx, 0)

The Jacobian of the corresponding pair can be computed using the collisionJacobian method

J = robot.collisionJacobian(idx, q)

The jacobian is a 1xN matrix (row matrix) corresponding to the contact normal. Take care that some information are stored in the visual objects when calling checkCollision, that are later used by collisionJacobian. You have to call collisionJacobian right after checkCollision, or the resulting jacobian might not be coherent.

For all collision pairs in contact (distance below 1e-3), the Jacobian must be collected and stacked in a single J matrix (which has as many rows as active constraints). Similarly, distances must be stacked in a vector (same number of rows as the jacobian).

Now, the joint acceleration is constrained by the contact constraint. It can be written as a minimization problem using Gauss principle

\(min \quad \frac{1}{2} (\ddot q - \ddot q_0 )^T M (\ddot q - \ddot q_0 )\)

\(s.t. \quad J \ddot q > 0 \)

where \(\ddot q_0\) is the free acceleration, i.e. the acceleration obtained in Question 2 where no constraint is active.

In theory, the acceleration should be above the "centrifugal" acceleration (i.e. the acceleration caused by joint velocity only, often written \(\dot J \dot q\)) but we neglect it here.

In case of penetration or negative velocity, having only position acceleration is not enough. A "trick" is often to require the contact acceleration to be above a proportional depending of the penetration distance: \(J \ddot q >= -dist\), with \(dist\) the vector of stacked distances.

Question 4

Implement a contact simulator using QuadProg, the results of Question 2 and the jacobian matrix of constraints whose distance is below 1e-3.

A better solution to avoid penetration is to implement an impact model. The simplest one is the inelastic impact, where normal velocity is simply canceled at impact. For that, remember inactive contact (i.e. those that were not in collision at previous simulation step). When a collision pair is detected that was not previously active, project the current velocity on the null space of all contacts:

\(\dot q = \dot q - J^+ J \dot q\)

Question 5

The complete loop should be as follows: \(\tau_q\) is computed from a PD tracking a time-varying joint position (question 3). After computing \(\tau_q\), all collision pairs must be checked to find those with distances below 1e-3. Corresponding Jacobians must be computed and stacked. If a new collision as appeared, the joint velocity must be projected to nullify it. If not collision is active, the joint acceleration is computed from inverting the mass matrix (question 2). Otherwise, it is computed using QuadProg (question 4). The resulting acceleration is integrated twice (question 1) before displaying the robot starting a new simulation iteration.