[ODE] Ragdoll characters with ODE

[Martin C. Martin]

Adam Paul Coates wrote:
> 
> policy/value iteration, simple search, even linear regression or neural
> nets can get you pretty close.

Policy/value iteration has real problems with more than a few degrees of
freedom, and is almost impossible with more than five degrees of freedom,
unless you can exploit a lot of knowledge about the problem.

What did you have in mind for linear regression?  That the torque would be
linear in the angles and angular velocities of the joints?  I can pretty
much guarantee you that won't work.  Also, you need to tell it the "right"
torque for a bunch of angle/angular velocity combos.  Where are you going
to get that data?

Neural nets are a representation, not a learning technique.  It's common
to choose the weights of a NN using policy/value iteration, evolution, or
the standard backprop algorithm.  For the backprop, you'll again need
training data.

What do you mean by "simple search"?  Given the above, I doubt anything
that counts as simple search would work.

>  In a class project last year, we "taught"
> an airplane to fly itself in a knife-edge, etc. using neural nets

That's much easier than walking.

> The --really-- great/horrible
> thing about most of these algorithms (and you could adapt GAs to do this)
> is that they usually give you a fat lookup-table that defines exactly what
> output to use for a given input.  i.e., current state goes in --> joint
> velocities come out.  Very very easy to implement, and very very fast at
> runtime.  The trouble, of course, is designing the character model and
> then coming up with this table, which for a large input space [e.g., if
> your input is the state of every joint] your table will be -huge-;

A lookup table is a representation, but only one possible representation. 
A neural net is far from a "huge lookup table."

For those who aren't afraid of reading a Ph.D. thesis, I'd suggest reading
those from the MIT leg lab, especially Mike Wessler's Ph.D. thesis.

- Martin