function [f,df] = tumbler_grad(x,oc,t,xt0,xtf,init_pos, init_vel,obs)

% globals:
global delta_t;
global Xg;
global order;
global start_const;
global end_const;
global Xpass;
global Xvel;
global Tspan;


% no inequality constraints:
dg = 0*x';

% do this so we can still get xf even in we stop the proceedure with cntl-c
global xf;
xf = x;

% we need to take the free parameters contained in x and the information
%   contained in xt0 and xtf and construct a spline.

m = length(xt0);
temp = [];
for i=1:m:length(x)-m+1
  temp = [temp; x(i:i+m-1)];
end
temp = temp';

% xa contains the full matrix of spline multipliers which define the 
%   active joint path
m1 = xt0 * ones(1,start_const);
m2 = xtf * ones(1,end_const);

xa = [m1 temp m2];

pth = wlpath;
pth = construct(pth,t,xa,order);

nj = numjoints(oc);
np = numpassive(oc);
na = nj - np;
num_param = length(x);

v0 = zeros(2*np + 1 + num_param*(2*np + 1),1);

% init_pos and init_vel contain the initial position and velocity for
%   the passive joints
v0(1:np)      = init_pos;
v0(np+1:2*np) = init_vel;

myopt = odeset('RelTol',1e-4,'AbsTol',1e-7,'MaxStep',0.1);
[T,X] = ode45('linksimgrad',[t(1):delta_t:t(length(t))],v0, ...
               myopt,oc,pth,obs);
n = length(T);
%size(X)


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% f is the objective function
% df is the derivative of the objective function
%
% To know what the index is for the df derivative terms, can use a line
% like the following:
% 

f = -X(n,1) + 20*(X(n,2) - 0)^2 + 10*X(n,5)
df = -X(n,6:12) +2*20*(X(n,2) - 0)*X(n,20:26) + 10*X(n,34:40)


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% store the stuff we got in some global variables so we retain this
%  if the optimization is stopped prematurely
Xpass = X(:,1:np);
Xvel = X(:,(np+1):(2*np)); % we need velocities for later analysis
Tspan = T;
%Xc = X;

% store the integration information in a global:
Xg = X;
pause(.1);