diff --git a/examples/kinematics/README.md b/examples/kinematics/README.md
new file mode 100644
index 0000000..6a7dcfd
--- /dev/null
+++ b/examples/kinematics/README.md
@@ -0,0 +1,7 @@
+## Robot kinematics
+
+We provide examples on how to perform forward and inverse kinematics.
+
+References:
+[1] "Robotics: Modelling, Planning and Control", Siciliano et al., 2010
+[2] "Springer Handbook of Robotics", Siciliano et al., 2008
diff --git a/examples/kinematics/forward/data.txt b/examples/kinematics/forward/data.txt
new file mode 100644
index 0000000..7dbbd7b
--- /dev/null
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diff --git a/examples/kinematics/forward/fk.py b/examples/kinematics/forward/fk.py
new file mode 100644
index 0000000..fa813c0
--- /dev/null
+++ b/examples/kinematics/forward/fk.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+"""In this file, we perform forward kinematics using the Kuka robot.
+
+The Kuka robot just draw a circle in the air. The joint positions are in the `data.txt` file.
+"""
+
+import os
+import pickle
+from itertools import count
+
+from pyrobolearn.simulators import Bullet
+from pyrobolearn.worlds import BasicWorld
+from pyrobolearn.robots import KukaIIWA, Body
+
+
+# Create simulator
+sim = Bullet()
+
+# create world
+world = BasicWorld(sim)
+
+# create robot
+robot = KukaIIWA(sim)
+robot.print_info()
+
+# define useful variables for FK
+link_id = robot.get_end_effector_ids(end_effector=0)
+joint_ids = robot.joints  # actuated joint
+
+# load data
+with open(os.path.dirname(os.path.abspath(__file__)) + '/data.txt', 'rb') as f:
+    positions = pickle.load(f)
+
+# set initial joint position
+robot.reset_joint_states(q=positions[0])
+
+# draw a sphere at the position of the end-effector
+sphere = world.load_visual_sphere(position=robot.get_link_world_positions(link_id),
+                                  radius=0.05, color=(1, 0, 0, 0.5))
+sphere = Body(sim, body_id=sphere)
+
+# perform simulation
+for t in count():
+
+    # if no more joint positions, get out of the loop
+    if t > len(positions):
+        break
+
+    # set joint positions
+    robot.set_joint_positions(positions[t], joint_ids=joint_ids)
+
+    # make the sphere follow the end effector
+    sphere.position = robot.get_link_world_positions(link_id)
+
+    # step in simulation
+    world.step(sleep_dt=1./240)
diff --git a/examples/kinematics/inverse/ik.py b/examples/kinematics/inverse/ik.py
new file mode 100644
index 0000000..6f0f28a
--- /dev/null
+++ b/examples/kinematics/inverse/ik.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+"""In this file, we perform inverse kinematics using the Kuka robot.
+
+Set the `solver_flag` to a number between 0 and 1 (see lines [19,22]) to select which IK solver to select.
+0: use robot.calculate_inverse_kinematics()
+1: use damped-least-squares IK using Jacobian (provided by pybullet)
+"""
+
+import numpy as np
+from itertools import count
+
+from pyrobolearn.simulators import Bullet
+from pyrobolearn.worlds import BasicWorld
+from pyrobolearn.robots import KukaIIWA
+
+
+# select IK solver, by setting the flag:
+# 0 = pybullet + calculate_inverse_kinematics()
+# 1 = pybullet + damped-least-squares IK using Jacobian (provided by pybullet)
+solver_flag = 1  # 1 and 4 gives pretty good results
+
+
+# Create simulator
+sim = Bullet()
+
+# create world
+world = BasicWorld(sim)
+
+# create robot
+robot = KukaIIWA(sim)
+robot.print_info()
+
+# define useful variables for IK
+dt = 1./240
+link_id = robot.get_end_effector_ids(end_effector=0)
+joint_ids = robot.joints  # actuated joint
+damping = 0.01  # for damped-least-squares IK
+wrt_link_id = -1  # robot.get_link_ids('iiwa_link_1')
+
+# desired position
+xd = np.array([0.5, 0., 0.5])
+world.load_visual_sphere(xd, radius=0.05, color=(1, 0, 0, 0.5))
+
+# joint_ids = joint_ids[2:]
+
+robot.change_transparency()
+robot.draw_link_frames([-1, 0])
+robot.draw_bounding_boxes(joint_ids[0])
+# robot.draw_link_coms([-1,0])
+
+qIdx = robot.get_q_indices(joint_ids)
+
+
+# OPTION 1: using `calculate_inverse_kinematics`###
+if solver_flag == 0:
+    for _ in count():
+        # get current position in the task/operational space
+        x = robot.get_link_world_positions(link_id)
+        # print("(xd - x) = {}".format(xd - x))
+
+        # perform full IK
+        q = robot.calculate_inverse_kinematics(link_id, position=xd)
+
+        # set the joint positions
+        robot.set_joint_positions(q[qIdx], joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
+
+# OPTION 2: using Jacobian and manual damped-least-squares IK ###
+elif solver_flag == 1:
+    kp = 50    # 5 if velocity control, 50 if position control
+    kd = 0     # 2*np.sqrt(kp)
+
+    for _ in count():
+        # get current position in the task/operational space
+        x = robot.get_link_world_positions(link_id)
+        dx = robot.get_link_world_linear_velocities(link_id)
+        # print("(xd - x) = {}".format(xd - x))
+
+        # Get joint configuration
+        q = robot.get_joint_positions()
+
+        # Get linear jacobian
+        if robot.has_floating_base():
+            J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6]
+        else:
+            J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx]
+
+        # Pseudo-inverse
+        # Jp = robot.get_pinv_jacobian(J)
+        # Jp = J.T.dot(np.linalg.inv(J.dot(J.T) + damping*np.identity(3)))   # this also works
+        Jp = robot.get_damped_least_squares_inverse(J, damping)
+
+        # evaluate damped-least-squares IK
+        dq = Jp.dot(kp * (xd - x) - kd * dx)
+
+        # set joint velocities
+        # robot.set_joint_velocities(dq)
+
+        # set joint positions
+        q = q[qIdx] + dq * dt
+        robot.set_joint_positions(q, joint_ids=joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
diff --git a/examples/kinematics/inverse/ik_libraries.py b/examples/kinematics/inverse/ik_libraries.py
new file mode 100644
index 0000000..7839b30
--- /dev/null
+++ b/examples/kinematics/inverse/ik_libraries.py
@@ -0,0 +1,318 @@
+#!/usr/bin/env python
+"""In this file, we use and compare several IK libraries using the Kuka robot.
+
+Namely, we compare: pybullet, PyKDL, trac_ik, and rbdl.
+
+Set the `solver_flag` to a number between 0 and 4 (see lines [53,60]) to select which solver to select.
+0 = pybullet + calculate_inverse_kinematics()
+1 = pybullet + damped-least-squares IK using Jacobian (provided by pybullet)
+2 = PyKDL
+3 = trac_ik
+4 = rbdl + damped-least-squares IK using Jacobian (provided by rbdl)
+"""
+
+import os
+import numpy as np
+from itertools import count
+
+from pyrobolearn.simulators import Bullet
+from pyrobolearn.worlds import BasicWorld
+from pyrobolearn.robots import KukaIIWA
+
+
+# import PyKDL
+try:
+    import PyKDL as kdl
+except ImportError as e:
+    raise ImportError(repr(e) + '\nTry to install `PyKDL`: '
+                                'sudo apt-get install ros-<distribution>-python-orocos-kdl'
+                                'or install it manually from `https://github.com/orocos/orocos_kinematics_dynamics`')
+
+# import kdl_parser_py
+try:
+    import kdl_parser_py.urdf as KDLParser
+except ImportError as e:
+    raise ImportError(repr(e) + '\nTry to install `kdl_parser_py`: '
+                                'sudo apt-get install ros-<distribution>-kdl-parser-py')
+
+# import track_ik_python
+try:
+    from trac_ik_python.trac_ik import IK as TracIK
+except ImportError as e:
+    raise ImportError(repr(e) + '\nTry to install `trac_ik_python`: '
+                                'sudo apt-get install ros-<distribution>-trac-ik-python')
+
+# import rbdl
+try:
+    import rbdl
+except ImportError as e:
+    raise ImportError(repr(e) + '\nTry to install `rbdl` manually from `https://bitbucket.org/rbdl/rbdl`')
+
+
+# TO BE SET BY THE USER
+# select IK solver, by setting the flag:
+# 0 = pybullet + calculate_inverse_kinematics()
+# 1 = pybullet + damped-least-squares IK using Jacobian (provided by pybullet)
+# 2 = PyKDL
+# 3 = trac_ik
+# 4 = rbdl + damped-least-squares IK using Jacobian (provided by rbdl)
+solver_flag = 1  # 1 and 4 gives pretty good results
+
+
+# Create simulator
+sim = Bullet()
+
+# create world
+world = BasicWorld(sim)
+
+# create robot
+robot = KukaIIWA(sim)
+
+# define useful variables for IK
+dt = 1./240
+link_id = robot.get_end_effector_ids(end_effector=0)
+joint_ids = robot.joints  # actuated joint
+base_name = robot.base_name
+end_effector_name = robot.get_link_names(link_id)
+urdf = os.path.dirname(os.path.abspath(__file__)) + '/../../../pyrobolearn/robots/urdfs/kuka/kuka_iiwa/iiwa14.urdf'
+damping = 0.01  # for damped-least-squares IK
+wrt_link_id = -1  # robot.get_link_ids('iiwa_link_1')
+chain_name = robot.get_link_names(joint_ids)
+
+# desired position
+xd = np.array([0.5, 0., 0.5])
+world.load_visual_sphere(xd, radius=0.05, color=(1, 0, 0, 0.5))
+
+# joint_ids = joint_ids[2:]
+
+# print information about the robot
+print("")
+print("Robot: {}".format(robot))
+print("Number of DoFs: {}".format(robot.num_dofs))
+print("Joint ids: {}".format(robot.joints))
+print("Q Indices: {}".format(robot.get_q_indices()))
+print("Actuated joint ids: {}".format(robot.joints))
+print("Link names: {}".format(robot.get_link_names(robot.joints)))
+print("End-effector names: {}".format(robot.get_link_names(robot.get_end_effector_ids())))
+print("Floating base? {}".format(robot.has_floating_base()))
+print("Total mass = {} kg".format(robot.mass))
+print("")
+print("Base name for IK: {}".format(base_name))
+print("Link name for IK: {}".format(end_effector_name))
+print("Chain: {}".format(chain_name))
+print("")
+
+
+robot.change_transparency()
+robot.draw_link_frames([-1, 0])
+robot.draw_bounding_boxes(joint_ids[0])
+# robot.draw_link_coms([-1,0])
+
+qIdx = robot.get_q_indices(joint_ids)
+
+print(qIdx)
+print(joint_ids)
+
+
+#####################
+# IK using pybullet #
+#####################
+
+# OPTION 1: using `calculate_inverse_kinematics`###
+if solver_flag == 0:
+    for _ in count():
+        # # get current position in the task/operational space
+        # x = robot.get_link_positions(link_id, wrt_link_id)
+        x = robot.get_link_world_positions(link_id)
+        # print("(xd - x) = {}".format(xd - x))
+
+        # perform full IK
+        q = robot.calculate_inverse_kinematics(link_id, position=xd)
+
+        # set the joint positions
+        robot.set_joint_positions(q[qIdx], joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
+
+# OPTION 2: using Jacobian and manual damped-least-squares IK ###
+elif solver_flag == 1:
+    kp = 50    # 5 if velocity control, 50 if position control
+    kd = 0  # 2*np.sqrt(kp)
+
+    for _ in count():
+        # get current position in the task/operational space
+        # x = robot.get_link_positions(link_id, wrt_link_id)
+        # dx = robot.get_link_linear_velocities(link_id, wrt_link_id)
+        x = robot.get_link_world_positions(link_id)
+        dx = robot.get_link_world_linear_velocities(link_id)
+        print("(xd - x) = {}".format(xd - x))
+
+        # Get joint configuration
+        q = robot.get_joint_positions()
+
+        # Get linear jacobian
+        if robot.has_floating_base():
+            J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6]
+        else:
+            J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx]
+
+        # Pseudo-inverse
+        # Jp = robot.get_pinv_jacobian(J)
+        # Jp = J.T.dot(np.linalg.inv(J.dot(J.T) + damping*np.identity(3)))   # this also works
+        Jp = robot.get_damped_least_squares_inverse(J, damping)
+
+        # evaluate damped-least-squares IK
+        dq = Jp.dot(kp * (xd - x) - kd * dx)
+
+        # set joint velocities
+        # robot.set_joint_velocities(dq)
+
+        # set joint positions
+        q = q[qIdx] + dq * dt
+        robot.set_joint_positions(q, joint_idss=joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
+
+
+##################
+# IK using PyKDL #
+##################
+elif solver_flag == 2:
+    print("Using PyKDL:")
+    model = KDLParser.treeFromFile(urdf)
+    if model[0]:
+        model = model[1]
+    else:
+        raise ValueError("Error during the parsing")
+
+    # define the kinematic chain
+    chain = model.getChain(base_name, end_effector_name)
+    print("Number of joints in the chain: {}".format(chain.getNrOfJoints()))
+
+    # define the FK solver
+    FK = kdl.ChainFkSolverPos_recursive(chain)
+
+    # define the IK Solver
+    # IKV = kdl.ChainIkSolverVel_pinv(chain)
+    # IK = kdl.ChainIkSolverPos_NR(chain, FK, IKV)
+    IK = kdl.ChainIkSolverPos_LMA(chain)  # ,_maxiter=ik_max_iter, _eps_joints=ik_tol)
+
+    # desired final cartesian position
+    Fd = kdl.Frame(kdl.Vector(xd[0], xd[1], xd[2]))
+
+    for _ in count():
+        # get current position in the task/operational space
+        # x = robot.get_link_positions(link_id, wrt_link_id)
+        x = robot.get_link_world_positions(link_id)
+        print("(xd - x) = {}".format(xd - x))
+
+        # buffer to put the solution
+        q_solved = kdl.JntArray(chain.getNrOfJoints())
+
+        # initial joint positions
+        q = robot.get_joint_positions(joint_ids)
+        q_init = kdl.JntArray(chain.getNrOfJoints())
+        for i, j in enumerate(q):
+            q_init[i] = j
+
+        # Solve IK
+        IK.CartToJnt(q_init, Fd, q_solved)
+        q_solved = np.array([q_solved[i] for i in range(q_solved.rows())])
+
+        # set joint positions
+        robot.set_joint_positions(q_solved.tolist(), joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
+
+
+#####################
+# IK using track_ik #
+#####################
+elif solver_flag == 3:
+    # Documentation: https://bitbucket.org/traclabs/trac_ik/src/master/trac_ik_python/
+    # read urdf
+    urdf_string = open(urdf, 'r').read()
+
+    # create IK solver
+    ik_solver = TracIK(base_link=base_name, tip_link=end_effector_name, urdf_string=urdf_string, solve_type='Distance')
+
+    # define upper and lower limits (optional)
+    # lb, ub = -np.ones(6)*100, np.ones(6)*100
+    # ik_solver.set_joint_limits(lb, ub)
+
+    for _ in count():
+        # get current position in the task/operational space
+        # x = robot.get_link_positions(link_id, wrt_link_id)
+        x = robot.get_link_world_positions(link_id)
+        print("(xd - x) = {}".format(xd - x))
+
+        # get current joint configuration and orientation (the orientation has to be specified when using trac_ik)
+        q = robot.get_joint_positions(joint_ids)
+        quat = robot.get_link_world_orientations(link_id)
+
+        # get solution
+        q = ik_solver.get_ik(q, x=xd[0], y=xd[1], z=xd[2], rx=quat.x, ry=quat.y, rz=quat.z, rw=quat.w)
+
+        # set joint positions
+        if q is not None:
+            q = np.array(q)
+            robot.set_joint_positions(q, joint_ids)
+        else:
+            print('got None')
+
+        # step in the simulation
+        world.step(sleep_dt=dt)
+
+
+######################################################
+# IK using RBDL and own damped-least-squares inverse #
+######################################################
+elif solver_flag == 4:
+    # load model using rbdl
+    model = rbdl.loadModel(urdf, verbose=False, floating_base=False)
+    rbdl_link_id = model.GetBodyId('iiwa_link_7')  # end_effector_name)
+    rbdl_id = [model.GetBodyId(name) - 1 for name in chain_name]
+    print("RBDL Link ID: {}".format(rbdl_id))
+    print("RBDL Q size: {}".format(model.q_size))
+    print("RBDL number of DoFs: {}".format(model.dof_count))
+
+    J = np.zeros((3, model.dof_count))
+
+    def position_pd(x, xd, v, vd=np.zeros(3), ad=np.zeros(3), kp=100, kd=None):
+        # if damping is not specified, make it critically damped
+        if kd is None:
+            kd = 2.0 * np.sqrt(kp)
+
+        # return PD error
+        return kp * (xd - x) + kd * (vd - v) + ad
+
+    def damped_least_squares_ik(q, x, xd, v, damping, dt, kp=1, kd=None):
+        err = position_pd(x, xd, v, kp=kp, kd=kd)
+        body_point = np.zeros(3)
+        rbdl.CalcPointJacobian(model, q, rbdl_link_id, body_point, J, update_kinematics=True)
+        J_dagger = J.T.dot(np.linalg.inv(J.dot(J.T) + damping*np.identity(3)))
+        dq = J_dagger.dot(err)
+        q = q + dq * dt
+        return q
+
+    for _ in count():
+        # get current position in the task/operational space
+        # x = robot.get_link_positions(link_id, wrt_link_id)
+        x = robot.get_link_world_positions(link_id)
+        dx = robot.get_link_world_linear_velocities(link_id)
+        print("(xd - x) = {}".format(xd - x))
+
+        # get joint configuration
+        q = robot.get_joint_positions()
+
+        # get solution by performing damped-least-squares IK
+        q = damped_least_squares_ik(q, x, xd, dx, damping=0., dt=dt, kp=50, kd=0)
+
+        # set the joint positions
+        robot.set_joint_positions(q[rbdl_id], joint_ids)
+
+        # step in simulation
+        world.step(sleep_dt=dt)
diff --git a/examples/kinematics/inverse/moving_sphere.py b/examples/kinematics/inverse/moving_sphere.py
new file mode 100644
index 0000000..9fa12d9
--- /dev/null
+++ b/examples/kinematics/inverse/moving_sphere.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+"""Inverse kinematics with the Kuka robot where the goal is to follow a moving sphere.
+"""
+
+import numpy as np
+from itertools import count
+
+from pyrobolearn.simulators import Bullet
+from pyrobolearn.worlds import BasicWorld
+from pyrobolearn.robots import KukaIIWA, Body
+
+
+# Create simulator
+sim = Bullet()
+
+# create world
+world = BasicWorld(sim)
+
+# create robot
+robot = KukaIIWA(sim)
+robot.print_info()
+
+# define useful variables for IK
+dt = 1./240
+link_id = robot.get_end_effector_ids(end_effector=0)
+joint_ids = robot.joints  # actuated joint
+damping = 0.01  # for damped-least-squares IK
+wrt_link_id = -1  # robot.get_link_ids('iiwa_link_1')
+qIdx = robot.get_q_indices(joint_ids)
+
+# define gains
+kp = 50    # 5 if velocity control, 50 if position control
+kd = 0     # 2*np.sqrt(kp)
+
+# create sphere to follow
+sphere = world.load_visual_sphere(position=np.array([0.5, 0., 1.]), radius=0.05, color=(1, 0, 0, 0.5))
+sphere = Body(sim, body_id=sphere)
+
+# set initial joint positions (based on the position of the sphere at [0.5, 0, 1])
+robot.reset_joint_states(q=[8.84305270e-05, 7.11378917e-02, -1.68059886e-04, -9.71690439e-01, 1.68308810e-05,
+                            3.71467111e-01, 5.62890805e-05])
+
+# define amplitude and angular velocity when moving the sphere
+w = 0.01
+r = 0.2
+
+for t in count():
+    # move sphere
+    sphere.position = np.array([0.5, r * np.cos(w*t + np.pi/2), (1.-r) + r * np.sin(w*t + np.pi/2)])
+
+    # get current end-effector position and velocity in the task/operational space
+    x = robot.get_link_world_positions(link_id)
+    dx = robot.get_link_world_linear_velocities(link_id)
+
+    # Get joint positions
+    q = robot.get_joint_positions()
+
+    # Get linear jacobian
+    if robot.has_floating_base():
+        J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6]
+    else:
+        J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx]
+
+    # Pseudo-inverse
+    Jp = robot.get_damped_least_squares_inverse(J, damping)
+
+    # evaluate damped-least-squares IK
+    dq = Jp.dot(kp * (sphere.position - x) - kd * dx)
+
+    # set joint positions
+    q = q[qIdx] + dq * dt
+    robot.set_joint_positions(q, joint_ids=joint_ids)
+
+    # step in simulation
+    world.step(sleep_dt=dt)
diff --git a/pyrobolearn/robots/robot.py b/pyrobolearn/robots/robot.py
index 0b96bbe..80ab6c4 100644
--- a/pyrobolearn/robots/robot.py
+++ b/pyrobolearn/robots/robot.py
@@ -1546,7 +1546,7 @@ class Robot(ControllableBody):
                 # remove parent index from the end-effectors dict if present
                 end_effectors.pop(parent_idx, None)
 
-            self.end_effectors = end_effectors.keys()
+            self.end_effectors = list(end_effectors.keys())
             self.end_effector_names = {name: idx for idx, name in end_effectors.items()}
 
     def get_end_effector_ids(self, end_effector=None):