diff --git a/drake/examples/Acrobot/@AcrobotPlantCpp/manipulatorDynamics.cpp b/drake/examples/Acrobot/@AcrobotPlantCpp/manipulatorDynamics.cpp
index 6660c6fee360..ef60b75413be 100644
--- a/drake/examples/Acrobot/@AcrobotPlantCpp/manipulatorDynamics.cpp
+++ b/drake/examples/Acrobot/@AcrobotPlantCpp/manipulatorDynamics.cpp
@@ -3,7 +3,6 @@
 #include <cmath>
 #include <iostream>
 #include "drakeMexUtil.h"
-#include <unsupported/Eigen/AutoDiff>
 #include <drakeGradientUtil.h>
 
 using namespace Eigen;
@@ -50,89 +49,6 @@ void manipulatorDynamics(const mxArray* pobj, const MatrixBase<DerivedA> &q, con
   B << 0.0, 1.0;
 }
 
-template <typename Derived>
-Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime> autoDiffToValueMatrix(const Eigen::MatrixBase<Derived>& auto_diff_matrix) {
-  Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime> ret(auto_diff_matrix.rows(), auto_diff_matrix.cols());
-  for (int i = 0; i < auto_diff_matrix.rows(); i++) {
-    for (int j = 0; j < auto_diff_matrix.cols(); ++j) {
-      ret(i, j) = auto_diff_matrix(i, j).value();
-    }
-  }
-  return ret;
-};
-
-template<typename Derived>
-typename Gradient<Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime>, Eigen::Dynamic>::type autoDiffToGradientMatrix(
-        const Eigen::MatrixBase<Derived>& auto_diff_matrix, int num_variables = Eigen::Dynamic)
-{
-  int num_variables_from_matrix = 0;
-  for (int i = 0; i < auto_diff_matrix.size(); ++i) {
-    num_variables_from_matrix = std::max(num_variables_from_matrix, static_cast<int>(auto_diff_matrix(i).derivatives().size()));
-  }
-  if (num_variables == Eigen::Dynamic) {
-    num_variables = num_variables_from_matrix;
-  }
-  else if (num_variables_from_matrix != 0 && num_variables_from_matrix != num_variables) {
-    std::stringstream buf;
-    buf << "Input matrix has derivatives w.r.t " << num_variables_from_matrix << ", variables" << ", whereas num_variables is " << num_variables << ".\n";
-    buf << "Either num_variables_from_matrix should be zero, or it should match num_variables";
-    throw std::runtime_error(buf.str());
-  }
-
-  typename Gradient<Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime>, Eigen::Dynamic>::type gradient(auto_diff_matrix.size(), num_variables);
-  for (int row = 0; row < auto_diff_matrix.rows(); row++) {
-    for (int col = 0; col < auto_diff_matrix.cols(); col++) {
-      auto gradient_row = gradient.row(row + col * auto_diff_matrix.rows()).transpose();
-      if (auto_diff_matrix(row, col).derivatives().size() == 0) {
-        gradient_row.setZero();
-      } else {
-        gradient_row = auto_diff_matrix(row, col).derivatives();
-      }
-    }
-  }
-  return gradient;
-}
-
-template<typename DerivedGradient, typename DerivedAutoDiff>
-void gradientMatrixToAutoDiff(const Eigen::MatrixBase<DerivedGradient>& gradient, Eigen::MatrixBase<DerivedAutoDiff>& auto_diff_matrix)
-{
-  int nq = gradient.cols();
-  for (size_t row = 0; row < auto_diff_matrix.rows(); row++) {
-    for (size_t col = 0; col < auto_diff_matrix.cols(); col++) {
-      auto_diff_matrix(row, col).derivatives().resize(nq, 1);
-      auto_diff_matrix(row, col).derivatives() = gradient.row(row + col * auto_diff_matrix.rows()).transpose();
-    }
-  }
-}
-
-template<int Rows, int Cols>
-Eigen::Matrix<AutoDiffScalar<VectorXd>, Rows, Cols> taylorVarToEigen(const mxArray* taylor_var) {
-  auto f = mxGetPropertySafe(taylor_var, "f");
-  auto df = mxGetPropertySafe(taylor_var, "df");
-  if (mxGetNumberOfElements(df) > 1)
-    throw runtime_error("TaylorVars of order higher than 1 currently not supported");
-  auto ret = matlabToEigenMap<Rows, Cols>(f).template cast<AutoDiffScalar<VectorXd>>().eval();
-  typedef Gradient<decltype(ret), Dynamic> GradientType;
-  auto gradient_matrix = matlabToEigenMap<GradientType::type::RowsAtCompileTime, GradientType::type::ColsAtCompileTime>(mxGetCell(df, 0));
-  gradientMatrixToAutoDiff(gradient_matrix, ret);
-
-  return ret;
-}
-
-template <typename Derived>
-mxArray* eigenToTaylorVar(const MatrixBase<Derived>& m, int num_variables = Eigen::Dynamic)
-{
-  const int nrhs = 2;
-  mxArray *prhs[nrhs];
-  prhs[0] = eigenToMatlab(autoDiffToValueMatrix(m));
-  mwSize dims[] = {1};
-  prhs[1] = mxCreateCellArray(1, dims);
-  mxArray *plhs[1];
-  mxSetCell(prhs[1], 0, eigenToMatlab(autoDiffToGradientMatrix(m, num_variables)));
-  mexCallMATLABsafe(1, plhs, nrhs, prhs, "TaylorVar");
-  return plhs[0];
-}
-
 void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
 {
   const mxArray* pobj = prhs[0];
diff --git a/drake/util/CMakeLists.txt b/drake/util/CMakeLists.txt
index 889eda586339..35266a5c022a 100644
--- a/drake/util/CMakeLists.txt
+++ b/drake/util/CMakeLists.txt
@@ -33,7 +33,7 @@ pods_install_pkg_config_file(drake-util
 
 if (MATLAB_FOUND)
   add_mex(drakeMexUtil SHARED drakeMexUtil.cpp MexWrapper.cpp)
-  target_link_libraries(drakeMexUtil drakePolynomial)
+  target_link_libraries(drakeMexUtil drakePolynomial drakeGradientUtil)
   # todo: use this again once I can assume everyone has CMAKE version >= 2.8.8
   #add_mex(drakeUtil OBJECT drakeUtil.cpp)
 
diff --git a/drake/util/drakeGradientUtil.h b/drake/util/drakeGradientUtil.h
index 6a92c0bd20d2..56fa3d0f5c1d 100644
--- a/drake/util/drakeGradientUtil.h
+++ b/drake/util/drakeGradientUtil.h
@@ -128,4 +128,59 @@ template<int QSubvectorSize, typename DerivedDM, typename DerivedDMSub>
 DLLEXPORT void setSubMatrixGradient(Eigen::MatrixBase<DerivedDM>& dM, const Eigen::MatrixBase<DerivedDMSub>& dM_submatrix, int row, int col, typename DerivedDM::Index M_rows,
     typename DerivedDM::Index q_start = 0, typename DerivedDM::Index q_subvector_size = QSubvectorSize);
 
+template <typename Derived>
+Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime> autoDiffToValueMatrix(const Eigen::MatrixBase<Derived>& auto_diff_matrix) {
+  Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime> ret(auto_diff_matrix.rows(), auto_diff_matrix.cols());
+  for (int i = 0; i < auto_diff_matrix.rows(); i++) {
+    for (int j = 0; j < auto_diff_matrix.cols(); ++j) {
+      ret(i, j) = auto_diff_matrix(i, j).value();
+    }
+  }
+  return ret;
+};
+
+template<typename Derived>
+typename Gradient<Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime>, Eigen::Dynamic>::type autoDiffToGradientMatrix(
+        const Eigen::MatrixBase<Derived>& auto_diff_matrix, int num_variables = Eigen::Dynamic)
+{
+  int num_variables_from_matrix = 0;
+  for (int i = 0; i < auto_diff_matrix.size(); ++i) {
+    num_variables_from_matrix = std::max(num_variables_from_matrix, static_cast<int>(auto_diff_matrix(i).derivatives().size()));
+  }
+  if (num_variables == Eigen::Dynamic) {
+    num_variables = num_variables_from_matrix;
+  }
+  else if (num_variables_from_matrix != 0 && num_variables_from_matrix != num_variables) {
+    std::stringstream buf;
+    buf << "Input matrix has derivatives w.r.t " << num_variables_from_matrix << " variables, whereas num_variables is " << num_variables << ".\n";
+    buf << "Either num_variables_from_matrix should be zero, or it should match num_variables.";
+    throw std::runtime_error(buf.str());
+  }
+
+  typename Gradient<Eigen::Matrix<typename Derived::Scalar::Scalar, Derived::RowsAtCompileTime, Derived::ColsAtCompileTime>, Eigen::Dynamic>::type gradient(auto_diff_matrix.size(), num_variables);
+  for (int row = 0; row < auto_diff_matrix.rows(); row++) {
+    for (int col = 0; col < auto_diff_matrix.cols(); col++) {
+      auto gradient_row = gradient.row(row + col * auto_diff_matrix.rows()).transpose();
+      if (auto_diff_matrix(row, col).derivatives().size() == 0) {
+        gradient_row.setZero();
+      } else {
+        gradient_row = auto_diff_matrix(row, col).derivatives();
+      }
+    }
+  }
+  return gradient;
+}
+
+template<typename DerivedGradient, typename DerivedAutoDiff>
+void gradientMatrixToAutoDiff(const Eigen::MatrixBase<DerivedGradient>& gradient, Eigen::MatrixBase<DerivedAutoDiff>& auto_diff_matrix)
+{
+  int nx = gradient.cols();
+  for (size_t row = 0; row < auto_diff_matrix.rows(); row++) {
+    for (size_t col = 0; col < auto_diff_matrix.cols(); col++) {
+      auto_diff_matrix(row, col).derivatives().resize(nx, 1);
+      auto_diff_matrix(row, col).derivatives() = gradient.row(row + col * auto_diff_matrix.rows()).transpose();
+    }
+  }
+}
+
 #endif /* DRAKEGRADIENTUTIL_H_ */
diff --git a/drake/util/drakeMexUtil.h b/drake/util/drakeMexUtil.h
index 02c21055eb0b..d0da2f93416b 100644
--- a/drake/util/drakeMexUtil.h
+++ b/drake/util/drakeMexUtil.h
@@ -1,7 +1,9 @@
 #include "mex.h"
 #include <vector>
 #include <Eigen/Core>
+#include <unsupported/Eigen/AutoDiff>
 #include "TrigPoly.h"
+#include "drakeGradientUtil.h"
 
 #ifndef DRAKE_MEX_UTIL_H_
 #define DRAKE_MEX_UTIL_H_
@@ -207,5 +209,32 @@ mxArray* eigenToTrigPoly(const Eigen::Matrix<TrigPolyd,_Rows,_Cols> & trigpoly_m
   return plhs[0];
 }
 
+template<int Rows, int Cols>
+Eigen::Matrix<Eigen::AutoDiffScalar<Eigen::VectorXd>, Rows, Cols> taylorVarToEigen(const mxArray* taylor_var) {
+  auto f = mxGetPropertySafe(taylor_var, "f");
+  auto df = mxGetPropertySafe(taylor_var, "df");
+  if (mxGetNumberOfElements(df) > 1)
+    throw std::runtime_error("TaylorVars of order higher than 1 currently not supported");
+  auto ret = matlabToEigenMap<Rows, Cols>(f).template cast<Eigen::AutoDiffScalar<Eigen::VectorXd>>().eval();
+  typedef Gradient<decltype(ret), Eigen::Dynamic> GradientType;
+  auto gradient_matrix = matlabToEigenMap<GradientType::type::RowsAtCompileTime, GradientType::type::ColsAtCompileTime>(mxGetCell(df, 0));
+  gradientMatrixToAutoDiff(gradient_matrix, ret);
+  return ret;
+}
+
+template <typename Derived>
+mxArray* eigenToTaylorVar(const Eigen::MatrixBase<Derived>& m, int num_variables = Eigen::Dynamic)
+{
+  const int nrhs = 2;
+  mxArray *prhs[nrhs];
+  prhs[0] = eigenToMatlab(autoDiffToValueMatrix(m));
+  mwSize dims[] = {1};
+  prhs[1] = mxCreateCellArray(1, dims);
+  mxArray *plhs[1];
+  mxSetCell(prhs[1], 0, eigenToMatlab(autoDiffToGradientMatrix(m, num_variables)));
+  mexCallMATLABsafe(1, plhs, nrhs, prhs, "TaylorVar");
+  return plhs[0];
+}
+
 
 #endif