diff --git a/irlc/project2/Latex/02465project2_handin.tex b/irlc/project2/Latex/02465project2_handin.tex
new file mode 100644
index 0000000000000000000000000000000000000000..045ca4d444de31ffaabdd612bd38a7d7e207cab4
--- /dev/null
+++ b/irlc/project2/Latex/02465project2_handin.tex
@@ -0,0 +1,146 @@
+\documentclass[12pt,twoside]{article}
+%\usepackage[table]{xcolor} % important to avoid options clash.
+%\input{02465shared_preamble}
+%\usepackage{cleveref}
+\usepackage{url}
+\usepackage{graphics}
+\usepackage{multicol}
+\usepackage{rotate}
+\usepackage{rotating}
+\usepackage{booktabs}
+\usepackage{hyperref}
+\usepackage{pifont}
+\usepackage{latexsym}
+\usepackage[english]{babel}
+\usepackage{epstopdf}
+\usepackage{etoolbox}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{multirow,epstopdf}
+\usepackage{fancyhdr}
+\usepackage{booktabs}
+\usepackage{xcolor}
+\newcommand\redt[1]{ {\textcolor[rgb]{0.60, 0.00, 0.00}{\textbf{ #1} } } }
+
+
+\newcommand{\m}[1]{\boldsymbol{ #1}}
+\newcommand{\yoursolution}{ \redt{(your solution here) } } 
+
+
+
+\title{ Report 2 hand-in }
+\date{ \today }
+\author{Alice (\texttt{s000001})\and  Bob (\texttt{s000002})\and Clara (\texttt{s000003}) } 
+
+\begin{document}
+\maketitle
+
+\begin{table}[ht!]
+\caption{Attribution table. Feel free to add/remove rows and columns}
+\begin{tabular}{llll}
+\toprule
+                                                                    & Alice   & Bob    & Clara   \\
+\midrule
+ 1: Formulate Yodas pendulum as a linear problem                    & 0-100\%  & 0-100\% & 0-100\%  \\
+ 2: State at a later time                                           & 0-100\%  & 0-100\% & 0-100\%  \\
+ 3: State at a later time II                                        & 0-100\%  & 0-100\% & 0-100\%  \\
+ 4: Eigenvalues and powers                                          & 0-100\%  & 0-100\% & 0-100\%  \\
+ 5: Analytical expression of Eigenvalues using Euler discretization & 0-100\%  & 0-100\% & 0-100\%  \\
+ 6: Bound using Euler discretization                                & 0-100\%  & 0-100\% & 0-100\%  \\
+ 7: Matrix norm of Exponential discretization (harder)              & 0-100\%  & 0-100\% & 0-100\%  \\
+ 8: Stability                                                       & 0-100\%  & 0-100\% & 0-100\%  \\
+ 9: Discretization                                                  & 0-100\%  & 0-100\% & 0-100\%  \\
+ 10: Linearization                                                  & 0-100\%  & 0-100\% & 0-100\%  \\
+ 11: Unitgrade self-check                                           & 0-100\%  & 0-100\% & 0-100\%  \\
+ 12: Optimal planning                                               & 0-100\%  & 0-100\% & 0-100\%  \\
+ 13: Control using simple linearization                             & 0-100\%  & 0-100\% & 0-100\%  \\
+ 14: MPC                                                            & 0-100\%  & 0-100\% & 0-100\%  \\
+\bottomrule
+\end{tabular}
+\end{table}
+
+%\paragraph{Statement about collaboration:}
+%Please edit this section to reflect how you have used external resources. The following statement will in most cases suffice: 
+%\emph{The code in the irls/project1 directory is entirely}
+
+%\paragraph{Main report:}
+Headings have been inserted in the document for readability. You only have to edit the part which says \yoursolution. 
+
+\section{Master Yodas pendulum (\texttt{yoda.py})}\label{yoda1}
+\subsubsection*{{\color{red}Problem 1:  Formulate Yodas pendulum as a linear problem}}
+	
+	\begin{align}
+		A & = \begin{bmatrix} \cdots \end{bmatrix}  \\
+		B & = \begin{bmatrix} \cdots \end{bmatrix}
+	\end{align}
+	\yoursolution 	
+	
+\subsubsection*{{\color{red}Problem 2:  State at a later time}}
+	
+		To solve the first part, we can write $\m x_N = \begin{bmatrix} \cdots \end{bmatrix}$
+		
+		As for the second part we get:
+\begin{align}
+\tilde A_0 & = \begin{bmatrix} \cdots \end{bmatrix}, \quad A_0 = \begin{bmatrix} \cdots \end{bmatrix}
+\end{align}
+		\yoursolution 	
+\subsubsection*{{\color{red}Problem 4:  Eigenvalues and powers}}
+	
+Assume $\lambda_1, \lambda_2$ are the eigenvalues ... then the Eigenvalues of $M$ is ... similarly for $\tilde M$ ... 
+\yoursolution
+	
+\subsubsection*{{\color{red}Problem 5:  Analytical expression of Eigenvalues using Euler discretization}}
+
+... we get a characteristic polynomial of ... and therefore it follows from Mat1 that the two Eigenvalues are ... 
+\yoursolution
+
+\subsubsection*{{\color{red}Problem 6:  Bound using Euler discretization}}
+
+	Using Euler discretization we get the upper bound:
+	$$
+\| \m x_N \| \leq \cdots
+$$
+\yoursolution
+	
+\subsubsection*{{\color{red}Problem 7:  Matrix norm of Exponential discretization (harder)}}
+	
+Using exponential discretization we get an upper bound of:
+		$$
+		\| \m x_N \| \leq \cdots
+		$$
+		\yoursolution
+	
+\section{R2D2 and control (\texttt{r2d2.py})}
+\subsubsection*{{\color{red}Problem 9:  Discretization}}
+
+	$$
+	\m x_{k+1} = \m f_k(\m x_k, \m u_k) = \begin{bmatrix} \cdots \\ \cdots \\ \cdots \end{bmatrix}$$
+
+\subsubsection*{{\color{red}Problem 10:  Linearization}}
+	
+$$
+		\m x_{k+1} \approx \begin{bmatrix} \cdots \\ \cdots \\ \cdots \end{bmatrix} \m x_k + 
+		\begin{bmatrix} \cdots \\ \cdots \\ \cdots \end{bmatrix} \m u_k +  
+		\begin{bmatrix} \vdots \end{bmatrix} 
+$$
+	
+\subsubsection*{{\color{red}Problem 12:  Optimal planning}}
+		
+	\begin{center}\includegraphics[width=.5\linewidth]{figures/your_answer}~
+		\includegraphics[width=.5\linewidth]{figures/your_answer} \end{center}
+
+\subsubsection*{{\color{red}Problem 13:  Control using simple linearization}}
+			
+		% Just generate the figures using the script and change the path below. 
+		\begin{center}\includegraphics[width=.5\linewidth]{figures/your_answer}~
+		\includegraphics[width=.5\linewidth]{figures/your_answer} \end{center}
+Intuitively, the second case fails because... \yoursolution	
+	
+\subsubsection*{{\color{red}Problem 14:  MPC}}
+			
+		\begin{center}\includegraphics[width=.6\linewidth]{figures/your_answer}%~
+		%	\includegraphics[width=.5\linewidth]{figures/your_answer} 
+	\end{center}
+		Iterative linearization solves the problem because... \yoursolution
+	
+\end{document}
\ No newline at end of file
diff --git a/irlc/project2/Latex/figures/your_answer.pdf b/irlc/project2/Latex/figures/your_answer.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d8c092974e20aaaf1165958a53bdce3a2ebdbf8f
Binary files /dev/null and b/irlc/project2/Latex/figures/your_answer.pdf differ
diff --git a/irlc/project2/__init__.py b/irlc/project2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8794db4fc72b62ae50ebe61fd5ce31a77a77992e
--- /dev/null
+++ b/irlc/project2/__init__.py
@@ -0,0 +1,2 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""This file is required for the test system but should otherwise be empty."""
diff --git a/irlc/project2/project2_grade.py b/irlc/project2/project2_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cc843924483550209ec050f7548e1aa94becade
--- /dev/null
+++ b/irlc/project2/project2_grade.py
@@ -0,0 +1,4 @@
+# irlc/project2/project2_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/irlc/project2/project2_tests.py b/irlc/project2/project2_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b43727d460d025580c4a71c45e6da655252f98a
--- /dev/null
+++ b/irlc/project2/project2_tests.py
@@ -0,0 +1,184 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from unitgrade import UTestCase, Report
+import irlc
+import numpy as np
+
+class YodaProblem1(UTestCase):
+    """ Test the get_A_B() function (Section 1, Problem 1) """
+    def test_A_B(self):
+        from irlc.project2.yoda import get_A_B
+        for g in [9.82, 5.1]:
+            for L in [0.2, 0.5, 1.1]:
+                A,B = get_A_B(g,L)
+                # To get the expected output of a test (in the cases where it is not specified manually),
+                # simply use the line self.get_expected_test_value() *right before* running the test-function itself.
+                # print("The expected value is", self.get_expected_test_value())
+                # If the code does not work, you need to upgrade unitgrade to the most recent version:
+                # pip install unitgrade --upgrade --no-cache
+                print(A)
+                self.assertLinf(A)
+                print(B)
+                self.assertLinf(B)
+
+class YodaProblem2(UTestCase):
+    r""" Yodas pendulum: Problem 2 """
+    def test_A0(self):
+        from irlc.project2.yoda import A_ei, A_euler
+        for g in [9.2, 10]:
+            for L in [0.2, 0.4]:
+                for Delta in [0.1, 0.2]:
+                    self.assertLinf(A_euler(g, L, Delta)) # Test Euler discretization
+                    self.assertLinf(A_ei(g, L, Delta))    # Test exponential discretization
+
+
+class YodaProblem3(UTestCase):
+    r""" Yodas pendulum: Problem 3 """
+    def test_M(self):
+        from irlc.project2.yoda import M_ei, M_euler
+        for g in [9.2, 10]:
+            for L in [0.2, 0.4]:
+                for Delta in [0.1, 0.2]:
+                    for N in [3, 5]:
+                        self.assertLinf(M_ei(g, L, Delta, N)) # Test Euler discretization
+                        self.assertLinf(M_euler(g, L, Delta, N))    # Test exponential discretization
+
+
+class YodaProblem6(UTestCase):
+    r""" Yodas pendulum: Bound using Euler discretization Problem 6 """
+    def test_xN_euler_bound(self):
+        from irlc.project2.yoda import xN_bound_euler
+        for g in [9.2, 10]:
+            for L in [0.2, 0.4]:
+                for Delta in [0.1, 0.2]:
+                    for N in [3, 5]:
+                        self.assertLinf(xN_bound_euler(g, L, Delta, N))
+
+class YodaProblem7(UTestCase):
+    r"""Yodas pendulum: Bound using exponential discretization Problem 7 """
+    def test_xN_euler_bound(self):
+        from irlc.project2.yoda import xN_bound_ei
+        for g in [9.2, 10]:
+            for L in [0.2, 0.4]:
+                for Delta in [0.1, 0.2]:
+                    for N in [3, 5]:
+                        self.assertLinf(xN_bound_ei(g, L, Delta, N))
+
+
+class R2D2Problem15(UTestCase):
+    r"""R2D2: Tests the linearization and discretization code in Problem 9 and Problem 10"""
+    def test_f_euler_zeros(self):
+        # Test in a simple case:
+        x = np.zeros((3,))
+        u = np.asarray([1,0])
+        from irlc.project2.r2d2 import f_euler
+        self.assertLinf(f_euler(x, u, Delta=0.05))
+        self.assertLinf(f_euler(x, u, Delta=0.1))
+
+    def test_f_euler(self):
+        np.random.seed(42)
+        for _ in range(4):
+            x = np.random.randn(3)
+            u = np.random.randn(2)
+            from irlc.project2.r2d2 import f_euler
+            self.assertLinf(f_euler(x, u, Delta=0.05))
+            self.assertLinf(f_euler(x, u, Delta=0.1))
+
+    def checklin(self, x_bar, u_bar):
+        from irlc.project2.r2d2 import linearize
+        A, B, d = linearize(x_bar, u_bar, Delta=0.05)
+        self.assertLinf(A)
+        self.assertLinf(B)
+        self.assertLinf(d)
+
+    def test_linearization1(self):
+        x_bar = np.asarray([0, 0, 0])
+        u_bar = np.asarray([1, 0])
+        self.checklin(x_bar, u_bar)
+
+    def test_linearization2(self):
+        x_bar = np.asarray([0, 0, 0.24])
+        u_bar = np.asarray([1, 0])
+        self.checklin(x_bar, u_bar)
+
+    def test_linearization3(self):
+        np.random.seed(42)
+        for _ in range(10):
+            x_bar = np.random.randn(3)
+            u_bar = np.asarray([1, 0])
+            self.checklin(x_bar, u_bar)
+
+class R2D2Direct(UTestCase):
+    r"""Problem 12: R2D2 and direct methods """
+    def chk_direct(self, x_target):
+        from irlc.project2.r2d2 import drive_to_direct
+        states = drive_to_direct(x_target=x_target, plot=False)
+        self.assertIsInstance(states, np.ndarray)  # Test states are an ndarray
+        self.assertEqualC(states.shape)  # Test states have the right shape
+        self.assertL2(states, tol=0.03)
+
+    def test_direct_1(self):
+        x_target = (2, 0, 0)
+        self.chk_direct(x_target)
+
+    def test_direct_2(self):
+        x_target = (2, 2, np.pi / 2)
+        self.chk_direct(x_target)
+
+
+class R2D2Linearization(UTestCase):
+    """Problem 13: R2D2 and simple linearization."""
+    def chk_linearization(self, x_target):
+        from irlc.project2.r2d2 import drive_to_linearization
+        states = drive_to_linearization(x_target=x_target, plot=False)
+        self.assertIsInstance(states, np.ndarray)  # Test states are an ndarray
+        self.assertEqualC(states.shape)  # Test states have the right shape
+        self.assertL2(states, tol=0.03)
+
+    def test_linearization_1(self):
+        x_target = (2, 0, 0)
+        self.chk_linearization(x_target)
+
+    def test_linearization_2(self):
+        x_target = (2, 2, np.pi / 2)
+        self.chk_linearization(x_target)
+
+class R2D2_MPC(UTestCase):
+    r"""Problem 14: R2D2 and MPC."""
+    def chk_mpc(self, x_target):
+        from irlc.project2.r2d2 import drive_to_mpc
+        states = drive_to_mpc(x_target=x_target, plot=False)
+        self.assertIsInstance(states, np.ndarray)  # Test states are an ndarray
+        self.assertEqualC(states.shape)  # Test states have the right shape
+        self.assertL2(states, tol=0.03)
+
+    def test_mpc_1(self):
+        self.chk_mpc(x_target=(2,0,0) )
+
+    def test_mpc_2(self):
+        self.chk_mpc(x_target=(2, 2, np.pi / 2))
+
+class Project2(Report):
+    title = "Project part 2: Control"
+    pack_imports = [irlc]
+
+    yoda = [
+        (YodaProblem1, 10),
+        (YodaProblem2, 10),
+        (YodaProblem3, 10),
+        (YodaProblem6, 8),
+        (YodaProblem7, 2)
+             ]
+    r2d2 = [
+            (R2D2Problem15, 10),
+            (R2D2Direct, 10),
+            (R2D2Linearization, 10),
+            (R2D2_MPC, 10),
+            ]
+
+    questions = []
+    questions += yoda
+    questions += r2d2
+
+if __name__ == '__main__':
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Project2() )
diff --git a/irlc/project2/r2d2.py b/irlc/project2/r2d2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec8bf575b427bfc2cfc6376646eb023bc583eb0e
--- /dev/null
+++ b/irlc/project2/r2d2.py
@@ -0,0 +1,212 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+import time
+import numpy as np
+import sympy as sym
+import matplotlib.pyplot as plt
+from gymnasium.spaces import Box
+# matplotlib.use('Qt5Agg') This line may be useful if you are having matplotlib problems on Linux.
+from irlc.ex04.discrete_control_model import DiscreteControlModel
+from irlc.ex04.control_environment import ControlEnvironment
+from irlc.ex03.control_model import ControlModel
+from irlc.ex03.control_cost import SymbolicQRCost
+from irlc.ex05.direct_agent import DirectAgent
+from irlc.ex05.direct import get_opts, guess
+from irlc.ex07.linearization_agent import LinearizationAgent
+from irlc.project2.utils import R2D2Viewer
+from irlc import Agent, train, plot_trajectory, savepdf
+
+dt = 0.05 # Time discretization Delta
+Tmax = 5  # Total simulation time (in all instances). This means that N = Tmax/dt = 100.
+x22 = (2, 2, np.pi / 2)  # Where we want to drive to: x_target
+
+class R2D2Model(ControlModel): # This may help you get started.
+    state_labels = ["$x$", "$y$", r"$\gamma$"]
+    action_labels = ["Cart velocity $v$", r'Yaw rate $\omega$'] # Define constants as needed here (look at other environments); Note there is an easy way to add labels!
+
+    def __init__(self, x_target=(2,2,np.pi/2), Q0=1.): # This constructor is one possible choice.
+        # Q0:       The Q-matrix for the cF-term in the cost function (see problem description)
+        # x_target: The state we will drive towards.
+        self.x_target = np.asarray(x_target)
+        self.Q0 = Q0
+        self.Tmax = 5  # Plan for a maximum of 5 seconds.
+        # Set up a variable for rendering (optional) and call superclass.
+        self.viewer = None
+        super().__init__()
+
+    def get_cost(self) -> SymbolicQRCost:
+        # The cost function uses self.Q0 to define the appropriate cost. It has the same meaning as the lecture description
+        cost = SymbolicQRCost(Q=np.zeros(3), R=np.eye(2))
+        cost += cost.goal_seeking_cost(x_target=self.x_target)*self.Q0
+        return cost
+
+    def tF_bound(self) -> Box:
+        return Box(self.Tmax, self.Tmax, shape=(1,))
+
+    def x0_bound(self) -> Box:
+        return Box(0, 0, shape=(self.state_size,))
+
+    def xF_bound(self) -> Box: 
+        # TODO: 1 lines missing.
+        raise NotImplementedError("Complete this function to specify the target of R2D2.")
+
+    # TODO: 3 lines missing.
+    raise NotImplementedError("Complete model dynamics here.")
+
+    """ These are two helper functions. They add rendering functionality so you can eventually use the environment as
+    
+    > env = R2D2Environment(render_mode='human') 
+    
+    and see a small animation. 
+    """
+    def close(self):
+        if self.viewer is not None:
+            self.viewer.close()
+
+    def render(self, x, render_mode="human"): 
+        if self.viewer is None:
+            self.viewer = R2D2Viewer(x_target=self.x_target) # Target is the red cross.
+        self.viewer.update(x)
+        time.sleep(0.05)
+        return self.viewer.blit(render_mode=render_mode) 
+
+
+class R2D2Environment(ControlEnvironment):
+    def __init__(self, Tmax=Tmax, Q0=0., x_target=x22, dt=None, render_mode=None):
+        assert dt is not None, "Remember to specify the discretization time!"
+        model = R2D2Model(Q0=Q0, x_target=x_target) # Create an R2D2 ControlModel with the given parameters.
+        dmodel = DiscreteControlModel(model, dt=dt)   # Create a discrete version of the R2D2 ControlModel
+        super().__init__(dmodel, Tmax=Tmax, render_mode=render_mode)
+
+# TODO: 9 lines missing.
+raise NotImplementedError("Your code here.")
+
+def f_euler(x : np.ndarray, u : np.ndarray, Delta=0.05) -> np.ndarray: 
+    """ Solve Problem 9. The function should compute
+    > x_next = f_k(x, u)
+    """
+    # TODO: 1 lines missing.
+    raise NotImplementedError("return next state")
+    return x_next
+
+def linearize(x_bar, u_bar, Delta=0.05):
+    """ Linearize R2D2's dynamics around the two vectors x_bar, u_bar
+    and return A, B, d so that
+
+    x_{k+1} = A x_k + B u_k + d (approximately).
+
+    The function should return linearization matrices A, B and d.
+    """
+    # Create A, B, d as numpy ndarrays.
+    # TODO: 4 lines missing.
+    raise NotImplementedError("Insert your solution and remove this error.")
+    return A, B, d
+
+def drive_to_linearization(x_target, plot=True): 
+    """
+    Plan in a R2D2 model with specific value of x_target (in the cost function). We use Q0=1.0.
+
+    this function will linearize the dynamics around xbar=0, ubar=0 to get a linear approximation of the model,
+    and then use that to plan on a horizon of N=50 steps to get a control law (L_0, l_0). This is then applied
+    to generate actions.
+
+    Plot is an optional parameter to control plotting. the plot_trajectory(trajectory, env) method may be useful.
+
+    The function should return the states visited as a (samples x state-dimensions) matrix, i.e. same format
+    as the default output of trajectories when you use train(...).
+
+    Hints:
+        * The control method is identical to one we have seen in the exercises/notes. You can re-purpose the code from that week.
+        * Remember to set Q0=1
+    """
+    # TODO: 7 lines missing.
+    raise NotImplementedError("Implement function body")
+    return traj[0].state
+
+def drive_to_direct(x_target, plot=False): 
+    """
+    Optimal planning in the R2D2 model with specific value of x_target using the direct method.
+    Remember that for this problem we set Q0=0, and implement x_target as an end-point constraint (see examples from exercises).
+
+    Plot is an optional parameter to control plotting, and to (optionally) visualize the environment using code such as::
+
+    env = R2D2Environment(..., render_mode='human' if plot else None)
+
+    For making the actual plot, the plot_trajectory(trajectory, env) method may be useful (see examples from exercises to see how labels can be specified)
+
+    The function should return the states visited as a (samples x state-dimensions) matrix, i.e. same format
+    as the default output of trajectories when you use train(...).
+
+    Hints:
+        * The control method (Direct method) is identical to what we did in the exercises, but you have to specify the options
+        to implement the correct grid-refinement of N=10, N=20 and N=40.
+        * Remember to set Q0=0.
+    """
+    # TODO: 10 lines missing.
+    raise NotImplementedError("Implement function body")
+    return traj[0].state
+
+def drive_to_mpc(x_target, plot=True) -> np.ndarray: 
+    """
+    Plan in a R2D2 model with specific value of x_target (in the cost function) using iterative MPC (see problem text).
+    Use Q0 = 1. in the cost function (see the R2D2 model class)
+
+    Plot is an optional parameter to control plotting. the plot_trajectory(trajectory, env) method may be useful.
+
+    The function should return the states visited as a (samples x state-dimensions) matrix, i.e. same format
+    as the default output of trajectories when you use train(...).
+
+    Hints:
+     * The control method is *nearly* identical to the linearization control method. Think about the differences,
+       and how a solution to one can be used in another.
+     * A bit more specific: Linearization is handled similarly to the LinearizationAgent, however, we need to update
+       (in each step) the xbar/ubar states/actions we are linearizing about, and then just use the immediate action computed
+       by the linearization agent.
+     * My approach was to implement a variant of the LinearizationAgent.
+    """
+    # TODO: 6 lines missing.
+    raise NotImplementedError("Implement function body")
+    return traj[0].state
+
+if __name__ == "__main__":
+    r2d2 = R2D2Model()
+    print(r2d2) # This will print out details of your R2D2 model.
+
+    # Check Problem 10
+    x = np.asarray( [0, 0, 0] )
+    u = np.asarray( [1,0])
+    print("x_k =", x, "u_k =", u, "x_{k+1} =", f_euler(x, u, dt))
+
+    A,B,d = linearize(x_bar=x, u_bar=u, Delta=dt)
+    print("x_{k+1} ~ A x_k + B u_k + d")
+    print("A:", A)
+    print("B:", B)
+    print("d:", d)
+
+    # Test the simple linearization method (Problem 12)
+    states = drive_to_direct(x22, plot=True)
+    savepdf('r2d2_direct')
+    plt.show()
+    # plt.close()
+    plt.figure()
+    # Build plot assuming that states is in the format (samples x coordinates-of-state).
+    plt.plot(states[:,0], states[:,1], 'k-', label="R2D2's (x, y) trajectory")
+    plt.legend()
+    plt.xlabel("x")
+    plt.ylabel("y")
+    savepdf('r2d2_direct_B')
+    plt.show()
+
+    # Test the simple linearization method (Problem 13)
+    drive_to_linearization((2,0,0), plot=True)
+    savepdf('r2d2_linearization_1')
+    plt.show()
+
+    drive_to_linearization(x22, plot=True)
+    savepdf('r2d2_linearization_2')
+    plt.show()
+
+    # Test iterative LQR (Problem 14)
+    state = drive_to_mpc(x22, plot=True)
+    print(state[-1])
+    savepdf('r2d2_iterative_1')
+    plt.show()
diff --git a/irlc/project2/unitgrade_data/R2D2Direct.pkl b/irlc/project2/unitgrade_data/R2D2Direct.pkl
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diff --git a/irlc/project2/unitgrade_data/YodaProblem7.pkl b/irlc/project2/unitgrade_data/YodaProblem7.pkl
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diff --git a/irlc/project2/utils.py b/irlc/project2/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..355be7a117d3babf8fc90c064fd7f4d67501a54f
--- /dev/null
+++ b/irlc/project2/utils.py
@@ -0,0 +1,53 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from irlc.utils.graphics_util_pygame import UpgradedGraphicsUtil, rotate_around
+import numpy as np
+
+""" This file contains code you can either use (or not) to render the R2D2 robot. class is already called correctly by your R2D2 class, 
+and you don't really have to think too carefully about what the code does unless you want to R2D2 to look better.
+"""
+
+
+class R2D2Viewer(UpgradedGraphicsUtil):
+    def __init__(self, x_target = (0,0)):
+        self.x_target = x_target
+        width = 800
+        self.scale = width / 1000
+        xlim = 3
+        self.dw = self.scale * 0.1
+        super().__init__(screen_width=width, xmin=-xlim, xmax=xlim, ymin=xlim, ymax=-xlim, title='R2D2')
+        self.xlim = xlim
+    def render(self):
+        # self.
+        self.draw_background(background_color=(255, 255, 255))
+        dw = self.dw
+        self.line("t1", (-self.xlim, 0), (self.xlim, 0), width=1, color=(0,) * 3)
+        self.line("t1", (0, -self.xlim), (0, self.xlim), width=1, color=(0,) * 3)
+
+
+        self.circle("r2d2", pos=(self.x[0], self.x[1]), r=24, outlineColor=(100, 100, 200), fillColor=(100, 100, 200))
+        self.circle("r2d2", pos=(self.x[0], self.x[1]), r=20, outlineColor=(100, 100, 200), fillColor=(150, 150, 255))
+        self.circle("r2d2", pos=(self.x[0], self.x[1]), r=2, outlineColor=(100, 100, 200), fillColor=(0,)*3)
+
+        dx = 0.13
+        dy = dx/2.5
+        wheel = [(-dx, dy), (dx, dy), (dx, -dy), (-dx, -dy) ]
+        ddy = 0.20
+        w1 = [ (x, y + ddy) for x, y in wheel]
+        w1 = rotate_around(w1, (0,0), angle=self.x[2] / np.pi * 180)
+
+        w2 = [(x, y - ddy) for x, y in wheel]
+        w2 = rotate_around(w2, (0, 0), angle=self.x[2] / np.pi * 180)
+
+
+        self.polygon("wheel1", coords=[ (x +  self.x[0], self.x[1] + y) for x, y in w1], filled=True, fillColor=(200,)*3, outlineColor=(100,)*3, closed=True)
+        self.polygon("wheel2", coords=[ (x +  self.x[0], self.x[1] + y) for x, y in w2], filled=True, fillColor=(200,)*3, outlineColor=(100,)*3, closed=True)
+
+        dc = 0.1
+        xx = self.x_target[0]
+        yy = self.x_target[1]
+        self.line("t1", (xx-dc, yy+dc), (xx+dc, yy-dc), width=4, color=(200, 100, 100))
+        self.line("t1", (xx-dc, yy-dc), (xx+dc, yy+dc), width=4, color=(200, 100, 100))
+
+
+    def update(self, x):
+        self.x = x
diff --git a/irlc/project2/yoda.py b/irlc/project2/yoda.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfb70a45d25ceb150827269a4abf0625aab29245
--- /dev/null
+++ b/irlc/project2/yoda.py
@@ -0,0 +1,97 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+import numpy as np
+from scipy.linalg import expm  # Computes the matrix exponential e^A for a square matrix A
+from numpy.linalg import matrix_power  # Computes A^n for matrix A and integer n
+
+
+def get_A_B(g : float, L: float, m=0.1): 
+    r""" Compute the two matrices A, B (see Problem 1) here and return them.
+    The matrices should be numpy ndarrays. """
+    # TODO: 2 lines missing.
+    raise NotImplementedError("Compute numpy matrices A and B here")
+    return A, B
+
+
+def A_euler(g : float,L : float, Delta : float) -> np.ndarray: 
+    r""" Compute \tilde{A}_0 (Euler discretization), see Problem 2.
+
+    Hints:
+        * get_A_B can perhaps save you a line or two.
+    """
+    # TODO: 2 lines missing.
+    raise NotImplementedError("Implement function body")
+    return A0_tilde
+
+def A_ei(g : float,L : float, Delta : float) -> np.ndarray: 
+    r""" Compute A_0 (Exponential discretization), see Problem 2
+
+    Hints:
+        * The special function expm(X) computes the matrix exponential e^X. See the lecture notes for more information.
+    """
+    # TODO: 2 lines missing.
+    raise NotImplementedError("Implement function body")
+    return A0
+
+def M_euler(g : float, L : float, Delta : float, N : int) -> np.ndarray: 
+    r""" Compute \tilde{M} (Euler discretization), see Problem 3
+    Hints:
+        * the matrix_power(X,n) function can compute expressions such as X^n where X is a square matrix and n is a number
+    """
+    # TODO: 1 lines missing.
+    raise NotImplementedError("Implement function body")
+    return M_tilde
+
+def M_ei(g : float,L : float, Delta : float, N : int) -> np.ndarray: 
+    r""" Compute M (Exponential discretization), see Problem 3 """
+    # TODO: 1 lines missing.
+    raise NotImplementedError("Implement function body")
+    return M
+
+def xN_bound_euler(g : float, L : float,Delta : float,N : int) -> float: 
+    r""" Compute upper bound on |x_N| when using Euler discretization, see Problem 6.
+    The function should just return a number.
+
+    Hints:
+        * This function uses all input arguments.
+    """
+    # TODO: 1 lines missing.
+    raise NotImplementedError("Implement function body")
+    return bound
+
+def xN_bound_ei(g: float,L : float,Delta : float,N : int) -> float: 
+    r""" Compute upper bound on |x_N| when using exponential discretization, see Problem 7.
+
+    Hints:
+        * This function does NOT use all input arguments.
+        * This will be the hardest problem to solve, but the easiest function to implement.
+    """
+    # TODO: 1 lines missing.
+    raise NotImplementedError("Implement function body")
+    return bound
+
+if __name__ == '__main__':
+    g = 9.82 # gravitational constant
+    L = 5 # Length of string
+    m = 0.1 # Mass of pendulum (in kg)
+    Delta = 0.3 # Time-discretization constant Delta (in seconds)
+    N = 100 # Time steps
+
+    # Solve Problem 2
+    print("A0_euler")
+    print(A_euler(g, L, Delta))
+
+    print("A0_ei")
+    print(A_ei(g, L, Delta))
+
+    # Solve Problem 3
+    print("M_euler")
+    print(M_euler(g, L, Delta, N))
+
+    print("M_ei")
+    print(M_ei(g, L, Delta, N))
+
+    # Solve Problem 7, upper bound on x_N using Euler discretization
+    print("|x_N| <= ", xN_bound_euler(g, L, Delta, N))
+
+    # Solve Problem 8, upper bound on x_N using Exponential discretization
+    print("|x_N| <= ", xN_bound_ei(g, L, Delta, N))