Whole Body control Tutorial: A dance

NAOqi Motion - Overview | API | Tutorial

naoNAO only - Do not use on jujuPepper


This tutorial explains how to use whole body Cartesian control API in order to make the robot dance with foot constraints, torso and arms Cartesian commands.


The tutorial is written in Python.


You can download the whole Body multiple effectors control example here: almotion_wbMultipleEffectors.py

For any troubleshooting linked to python, see Python SDK Install Guide section.

Code review

In this section we describe each important piece of code of the example.

NAOqi tools

First, we import some external libraries:
  • argparse: toolbox useful to define parameter
  • motion: some useful definitions such as FRAME.
  • almath: an optimized mathematic toolbox for robotics. For further details, see: libalmath API reference.
  • time: mainly for the function sleep
  • ALProxy: create proxy to motion and robotposture modules

Then, the proxy to ALMotion module is created. This proxy is useful to called ALMotion API.

''' Whole Body Motion: Multiple Effectors control '''

import argparse
import motion
import almath
import time
from naoqi import ALProxy

def main(robotIP, PORT=9559):
        Example of a whole body multiple effectors control "LArm", "RArm" and "Torso"
        Warning: Needs a PoseInit before executing
                 Whole body balancer must be inactivated at the end of the script

    motionProxy  = ALProxy("ALMotion", robotIP, PORT)
    postureProxy = ALProxy("ALRobotPosture", robotIP, PORT)

Initialization of the robot

When doing Cartesian control, it’s important to be sure that the robot is in a good configuration. To have the maximum range of control, the maximum stability and far away of singularity.
A PoseInit is a good posture before a Cartesian control of the Torso.

    # Wake up robot

    # Send robot to Stand Init
    postureProxy.goToPosture("StandInit", 0.5)

Whole body initialization

Here, we specify the whole body constraints:

  • enable whole body
  • both legs are fixed
  • balance on double support

    # Enable Whole Body Balancer
    isEnabled  = True

    # Legs are constrained fixed
    stateName  = "Fixed"
    supportLeg = "Legs"
    motionProxy.wbFootState(stateName, supportLeg)

    # Constraint Balance Motion
    isEnable   = True
    supportLeg = "Legs"
    motionProxy.wbEnableBalanceConstraint(isEnable, supportLeg)

Arms motion

This code is dedicated to create motion with left and right arms in parallel.
By executing only this code, you will see all the robot move, but only the arms effectors are controlled.

    useSensorValues = False

    # Arms motion
    effectorList = ["LArm", "RArm"]

    frame        = motion.FRAME_ROBOT

    # pathLArm
    pathLArm = []
    currentTf = motionProxy.getTransform("LArm", frame, useSensorValues)
    # 1
    target1Tf  = almath.Transform(currentTf)
    target1Tf.r2_c4 += 0.08 # y
    target1Tf.r3_c4 += 0.14 # z

    # 2
    target2Tf  = almath.Transform(currentTf)
    target2Tf.r2_c4 -= 0.05 # y
    target2Tf.r3_c4 -= 0.07 # z


    # pathRArm
    pathRArm = []
    currentTf = motionProxy.getTransform("RArm", frame, useSensorValues)
    # 1
    target1Tf  = almath.Transform(currentTf)
    target1Tf.r2_c4 += 0.05 # y
    target1Tf.r3_c4 -= 0.07 # z

    # 2
    target2Tf  = almath.Transform(currentTf)
    target2Tf.r2_c4 -= 0.08 # y
    target2Tf.r3_c4 += 0.14 # z


    pathList = [pathLArm, pathRArm]

    axisMaskList = [almath.AXIS_MASK_VEL, # for "LArm"
                    almath.AXIS_MASK_VEL] # for "RArm"

    coef       = 1.5
    timesList  = [ [coef*(i+1) for i in range(5)],  # for "LArm" in seconds
                   [coef*(i+1) for i in range(6)] ] # for "RArm" in seconds

    # called cartesian interpolation
    motionProxy.transformInterpolations(effectorList, frame, pathList, axisMaskList, timesList)

Torso motion

This code is dedicated to create Cartesian motion of the robot’s torso.
During execution the arms will be fixed in FRAME_ROBOT.
This is due to the fact that a previous called (positionInterpolation) on Arms effector has automatically set effector optimization to True ( wbEnableEffectorOptimization(Arms, True) ).

    # Torso Motion
    effectorList = ["Torso", "LArm", "RArm"]

    dy = 0.06
    dz = 0.06

    # pathTorso
    currentTf = motionProxy.getTransform("Torso", frame, useSensorValues)
    # 1
    target1Tf  = almath.Transform(currentTf)
    target1Tf.r2_c4 += dy
    target1Tf.r3_c4 -= dz

    # 2
    target2Tf  = almath.Transform(currentTf)
    target2Tf.r2_c4 -= dy
    target2Tf.r3_c4 -= dz

    pathTorso = []
    for i in range(3):

    pathLArm = [motionProxy.getTransform("LArm", frame, useSensorValues)]
    pathRArm = [motionProxy.getTransform("RArm", frame, useSensorValues)]

    pathList = [pathTorso, pathLArm, pathRArm]

    axisMaskList = [almath.AXIS_MASK_ALL, # for "Torso"
                    almath.AXIS_MASK_VEL, # for "LArm"
                    almath.AXIS_MASK_VEL] # for "RArm"

    coef       = 0.5
    timesList  = [
                  [coef*(i+1) for i in range(12)], # for "Torso" in seconds
                  [coef*12],                       # for "LArm" in seconds
                  [coef*12]                        # for "RArm" in seconds

        effectorList, frame, pathList, axisMaskList, timesList)

Release constraint and disable whole body

This part is essential and stops the whole body balancer.

    # Deactivate whole body
    isEnabled    = False

    # Send robot to Pose Init
    postureProxy.goToPosture("StandInit", 0.3)

    # Go to rest position