diff --git a/crocoddyl/__init__.py b/crocoddyl/__init__.py
index c5c1d4b9675378f9a0bb98bc813dafd35cfd0817..b92d7b7180e827dd9f96638148624a6b095e66e1 100644
--- a/crocoddyl/__init__.py
+++ b/crocoddyl/__init__.py
@@ -30,7 +30,7 @@ from .impact import (ActionDataImpact, ActionModelImpact, CostModelImpactCoM, Co
 from .integrated_action import (IntegratedActionDataEuler, IntegratedActionDataRK4, IntegratedActionModelEuler,
                                 IntegratedActionModelRK4)
 from .kkt import SolverKKT
-from .robots import getTalosPathFromRos, loadHyQ, loadTalos, loadTalosArm, loadTalosLegs, loadKinton, loadKintonArm, loadBorinotArm
+from .robots import getTalosPathFromRos, loadHyQ, loadTalos, loadTalosArm, loadTalosLegs, loadKinton, loadKintonArm, load2dofPlanar, loadHector
 from .shooting import ShootingProblem
 from .solver import SolverAbstract
 from .state import StateAbstract, StateNumDiff, StatePinocchio, StateVector
diff --git a/crocoddyl/robots.py b/crocoddyl/robots.py
index 6be2250e84b6f0255f6e3f869e6d227fb9f7388b..25a51a77ceca6108152fc7bf6769302132709c60 100644
--- a/crocoddyl/robots.py
+++ b/crocoddyl/robots.py
@@ -142,9 +142,15 @@ def loadKintonArm(modelPath='/opt/openrobots/share/example-robot-data'):
     robot = RobotWrapper.BuildFromURDF(modelPath + URDF_SUBPATH, [modelPath])
     return robot
 
-def loadBorinotArm(modelPath='/opt/openrobots/share/example-robot-data'):
-    URDF_FILENAME = "borinot_arm.urdf"
-    URDF_SUBPATH = "/borinot_arm/urdf/" + URDF_FILENAME
+def load2dofPlanar(modelPath='/home/pepms/robotics/other-tools/robot-data'):
+    URDF_FILENAME = "2dof_planar.urdf"
+    URDF_SUBPATH = "/2dof_planar/urdf/" + URDF_FILENAME
     robot = RobotWrapper.BuildFromURDF(modelPath + URDF_SUBPATH, [modelPath], None)
     robot.q0.flat = [np.pi]
     return robot
+
+def loadHector(modelPath='/home/pepms/robotics/other-tools/robot-data'):
+    URDF_FILENAME = "quadrotor_base.urdf"
+    URDF_SUBPATH = "/hector-description/urdf/" + URDF_FILENAME
+    robot = RobotWrapper.BuildFromURDF(modelPath + URDF_SUBPATH, [modelPath], pinocchio.JointModelFreeFlyer())
+    return robot
diff --git a/examples/notebooks/2DOFs.ipynb b/examples/notebooks/2DOFs.ipynb
index 0b50f6fd1aaa0906cb86cb847a7c28a77db7fae4..157ec9edfca7c36659d6b552e6fa88e3de5dce74 100644
--- a/examples/notebooks/2DOFs.ipynb
+++ b/examples/notebooks/2DOFs.ipynb
@@ -10,7 +10,7 @@
     "import pinocchio as pin\n",
     "import numpy as np\n",
     "\n",
-    "robot = loadBorinotArm()\n",
+    "robot = load2dofPlanar()\n",
     "robot.initViewer(loadModel=True)\n",
     "\n",
     "q0 = [3.14, 0]\n",
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -451,7 +451,7 @@
        " True)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -525,7 +525,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f57076b5390>"
+       "<matplotlib.legend.Legend at 0x7fcd29239310>"
       ]
      },
      "execution_count": 9,
@@ -534,7 +534,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 2 Axes>"
       ]
@@ -546,7 +546,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 2 Axes>"
       ]
diff --git a/examples/notebooks/acrobot.ipynb b/examples/notebooks/acrobot.ipynb
index d00dc24cbb54c9dd5fccebe6ff91495687c816d8..ec4b4a4cf70dae23480c15d7f388f86f68ff4037 100644
--- a/examples/notebooks/acrobot.ipynb
+++ b/examples/notebooks/acrobot.ipynb
@@ -10,7 +10,7 @@
     "import pinocchio as pin\n",
     "import numpy as np\n",
     "\n",
-    "robot = loadBorinotArm()\n",
+    "robot = load2dofPlanar()\n",
     "robot.initViewer(loadModel=True)\n",
     "\n",
     "q0 = [3.14, 0]\n",
diff --git a/examples/notebooks/kinton_flying_mission.ipynb b/examples/notebooks/kinton_flying_mission.ipynb
index d9ddf79b0464d95c2601a83732543d4283066103..26323dec68b7e7a9a972d3405dd6c0f2f90db91c 100644
--- a/examples/notebooks/kinton_flying_mission.ipynb
+++ b/examples/notebooks/kinton_flying_mission.ipynb
@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "# LOAD ROBOT\n",
-    "robot = loadKinton()\n",
+    "robot = loadHector()\n",
     "robot.initViewer(loadModel=True)\n",
     "robot.display(robot.q0)\n",
     "\n",
@@ -30,7 +30,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 28,
+=======
+   "execution_count": 12,
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,6 +92,7 @@
     "    terminalCostModel.addCost(name=\"pos\", weight=1e3, cost=goalTrackingCost)\n",
     "\n",
     "    # DIFFERENTIAL ACTION MODEL\n",
+<<<<<<< HEAD
     "    runningDmodel  = DifferentialActionModelActuated(rmodel, actModel, runningCostModel)\n",
     "    terminalDmodel = DifferentialActionModelActuated(rmodel, actModel, terminalCostModel)\n",
     "    runningModel  = IntegratedActionModelEuler(runningDmodel)\n",
@@ -96,11 +101,21 @@
     "    terminalModel.timeStep =  integrationStep\n",
     "    \n",
     "    return runningModel, terminalModel   "
+=======
+    "    dmodel = DifferentialActionModelActuated(rmodel, actModel, runningCostModel)\n",
+    "    model = IntegratedActionModelEuler(dmodel)\n",
+    "    model.timeStep =  integrationStep  \n",
+    "    return model   "
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 29,
+=======
+   "execution_count": 13,
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
    "metadata": {
     "scrolled": true
    },
@@ -134,6 +149,7 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 30,
    "metadata": {
     "scrolled": true
@@ -645,6 +661,31 @@
      "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
+=======
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (10,) (4,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-57dd4f295efb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# Solving it with the DDP algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, maxiter, init_xs, init_us, isFeasible, regInit)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                     \u001b[0;31m# t = time.time()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeDirection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m                     \u001b[0;31m# print \"TIME, Solving: Compute direction. Iteration \" + str(i) + \": \" + str(time.time()-t)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mArithmeticError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36mcomputeDirection\u001b[0;34m(self, recalc)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \"\"\"\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackwardPass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \"\"\" Compute the tangent (LQR) model.\n\u001b[1;32m     53\u001b[0m         \"\"\"\n\u001b[0;32m---> 54\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misFeasible\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0;31m# Gap store the state defect from the guess to feasible (rollout) trajectory, i.e.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/shooting.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, xs, us)\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningModels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m             \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/integrated_action.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mnv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mdxnext_dx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxnext_ddx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mState\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJintegrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/integrated_action.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mnq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithCostResiduals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcostResiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcostResiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/differential_action.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m    353\u001b[0m         \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforwardKinematics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdateFramePlacements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/cost.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0mnr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithResiduals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mncost\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresiduals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/cost.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mu\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m         \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresiduals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/pepms/robotics/toolboxes/crocoddyl/crocoddyl/activation.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, r)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (10,) (4,) "
+     ]
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
     }
    ],
    "source": [
@@ -653,7 +694,7 @@
     "    runningModel, terminalModel = uavPlacementModel(target_pos[i], target_quat[i], dt, 'base_link')\n",
     "    models += [runningModel]*(T-1) + [terminalModel]\n",
     "\n",
-    "q0 = rmodel.referenceConfigurations[\"initial_pose\"]\n",
+    "q0 = robot.q0\n",
     "x0 = np.hstack([m2a(q0), np.zeros(robot.model.nv)])\n",
     "\n",
     "problem = ShootingProblem(x0, models[:-1], models[-1])\n",
@@ -672,7 +713,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 31,
+=======
+   "execution_count": null,
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,28 +726,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "50"
+       "6"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "robot.nv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "np.size(ddp.xs,0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
+<<<<<<< HEAD
    "outputs": [
     {
      "ename": "AttributeError",
@@ -728,6 +783,9 @@
      "output_type": "display_data"
     }
    ],
+=======
+   "outputs": [],
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
    "source": [
     "distanceRotorCOG = 0.1525\n",
     "cf = 6.6e-5\n",
@@ -740,33 +798,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'set_title'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-6afaa0e0f774>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Moments'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'set_title'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "t = np.arange(0, 2*T*dt-dt, dt)\n",
     "control = np.vstack(ddp.us)\n",
@@ -792,7 +826,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
+<<<<<<< HEAD
    "version": "2.7.12"
+=======
+   "version": "2.7.15+"
+>>>>>>> c5bc015e713fd9ba79ee8d7d12db7b8003f36d3f
   }
  },
  "nbformat": 4,