LambdaCube 3D

Purely Functional Rendering Engine

Using Bullet physics with an FRP approach (part 2)

In the previous post, we introduced a simple attribute system to give the raw Bullet API a friendlier look. As it turns out, this system is easy to extend along the temporal dimension, since an attribute of a physical object is a time-varying value – implemented as a mutable variable under the hood. The common theme in various FRP approaches is about reifying the whole lifetime of such values in some form, which naturally leads to the idea of using the attributes as the bridge between the reactive library and the physics engine.

Time-varying attributes with Elerea

To make a seamless integration of Elerea and Bullet possible, we needed to define a few additional primitive signal constructors. Originally, the only way to feed data from IO sources into an Elerea network was through the external primitive, which constructed a signal and a corresponding IO function to update its output (the first argument is the initial value):

external :: a -> IO (Signal a, a -> IO ())

The obvious problem with external is the fact that it works outside the signal network, so it cannot be defined in a convenient way in terms of any entity that lives inside the reactive world. The simplest solution is to directly embed IO computations. One-off computations can be directly executed in SignalGen through the execute primitive, which is equivalent to liftIO. IO signals can be constructed with the effectful* family of functions, which are analogous to the applicative lifting combinators, but apply an IO function to the input signals instead of a pure one:

effectful :: IO a -> SignalGen (Signal a)
effectful1 :: (t -> IO a) -> Signal t -> SignalGen (Signal a)
effectful2 :: (t1 -> t2 -> IO a) -> Signal t1 -> Signal t2 -> SignalGen (Signal a)
...

The results must be in the SignalGen context in order to ensure that the constructed signals cause the IO computations to be evaluated exactly once per sampling step. The reason is simple: to meet this condition, the signal needs to be memoised, which requires an additional state variable, and state variables can only be introduced in SignalGen.

First, we extend the list of attribute operations with a fifth member, which defines the attribute to be updated by a signal. Since we might not want to take full control of the attribute, just intervene once in a while, the signal is required to be partial through Maybe. When the signal yields Nothing, the attribute is managed by Bullet during that superstep.

data AttrOp o = forall a . Attr o a := a
              | forall a . Attr o a :~ (a -> a)
              | forall a . Attr o a :!= IO a
              | forall a . Attr o a :!~ (a -> IO a)
              | forall a . Attr o a :< Signal (Maybe a)

Now we have all the building blocks necessary to define a signal-based variant of set from the last post:

set' :: o -> [AttrOp o] -> SignalGen (Signal ())
set' obj as = go as (return ())
  where
    go [] sig     = return sig
    go (a:as) sig = case a of
        Attr getter setter := x  ->
            execute (setter obj x >> return ()) >> go as sig
        Attr getter setter :~ f  ->
            execute (getter obj >>= setter obj . f >> return ()) >> go as sig
        Attr getter setter :!= x ->
            execute (x >>= setter obj >> return ()) >> go as sig
        Attr getter setter :!~ f ->
            execute (getter obj >>= f >>= setter obj >> return ()) >> go as sig
        Attr getter setter :< s  -> do     
            dummy <- flip effectful1 s $ \mx -> case mx of
                Nothing -> return ()
                Just x  -> setter obj x >> return ()
            go as (liftA2 const sig dummy)

The first four cases are unchanged, they just need to be wrapped with execute. The signal case is also straightforward: we use effectful1 to sample the signal and update the attribute with its current value. What might not be clear first is all the additional plumbing. Unfortunately, this is all necessary due to the fact that if a signal is not referenced anywhere in the system, it gets garbage collected.

In this case, effectful1 doesn’t produce any meaningful output. All we need is its side effects. However, we still need to store the dummy signal (a stream of unit values), otherwise the setter will only be updated until the next garbage collection round. We can think of the dummy signal as a thread that’s kept alive only as long as we have a handle to it. To ensure that the reference is not lost, we carefully wrap it in another dummy signal that keeps all the signal setters alive. It is up to the caller of set’ to store the final signal.

It is useful to define the equivalent of make as well:

make' :: IO o -> [AttrOp o] -> SignalGen (Signal o)
make' act flags = do
    obj <- execute act
    dummy <- set' obj flags
    return (liftA2 const (return obj) dummy)

To reduce the chance of user error, we return a signal that represents the object we just constructed. The signal always yields a reference to the object, and keeps all the signal attributes alive. To ensure correct behaviour, we should always sample this signal when querying the object, instead of saving the reference and passing it around. This is necessary for another reason as well: thanks to Elerea’s dependency handling, it makes sure that the signal attributes are updated before the object is queried.

A simple use case

The example scene contains two independent details: a falling brick whose collisions with a designated object cause reactions, and a ragdoll that can be dragged with the mouse. In the rest of this post we’ll concentrate on the brick.

The LambdaCube ‘FRP physics’ example

All the objects are created in the top level of the signal network, in the SignalGen monad. In this setup, the object used for collision detection is actually a ghost sphere, not a solid body. We’ll refer to it as the query space. Whenever the brick intersects this sphere, its position and orientation is reset, but its velocity is retained. First we create the sphere, which is just an ordinary IO operation, so we can execute it:

    querySpace <- execute $ do
        ghostObject <- make btPairCachingGhostObject
                       [ collisionFlags :~ (.|. e_btCollisionObject_CollisionFlags_CF_NO_CONTACT_RESPONSE)
                       , collisionShape := sphereShape ghostRadius
                       , worldTransform := Transform idmtx 0
                       ]
        btCollisionWorld_addCollisionObject dynamicsWorld ghostObject 1 (-1)
        return ghostObject

Afterwards, we create a signal that tells us in every frame which objects are colliding with the query space:

    collisions <- effectful $ collisionInfo querySpace

The collisionInfo function returns a list of tuples, where each member contains references to both bodies involved, plus some spatial information that we ignore in this scenario. This is not a library function; its full definition is part of the example. The exact details are not relevant to the topic of this post, as they are just a direct application of the Bullet API, so we’re not going to discuss it here.

Given the collision signal, we can now define the brick:

    let initBrickTrans = Transform idmtx (Vec3 2 20 (-3))
    brick <- do
        rec brick <- make' (snd <$> localCreateRigidBodyM dynamicsWorld 1 initBrickTrans (boxShape brickSize))
                     [worldTransform :< boolToMaybe initBrickTrans . bodyInCollision brickBody <$> collisions]
            brickBody <- snapshot brick
        return brick

We use make’ to invoke the constructor of the brick and define the temporal behaviour of its worldTransform attribute in a single step. Again, the details of the construction are not particularly interesting: all we need is a mass, an initial transformation (position and orientation), and a collision shape for starters.

The real magic happens in the attribute override. Given the signal that tells us who collides with the query space, we can derive the signal that describes how the world transform of the brick needs to be updated over time. This is achieved by mapping a pure function over the signal, which yields Nothing if brickBody is not involved in any of the collisions, and Just initBrickTrans if it is.

One interesting bit to note is the recursion needed for the above definition, which is made possible thanks to SignalGen being a MonadFix instance. In order to define the world transform update signal, we need a reference to the object that we’re just creating. The reference comes from taking a snapshot of the brick signal. Since the update signal doesn’t need to be sampled for the sake of constructing the object, we don’t end up in an infinite loop.

The role of dummy signals

While the general idea of using signals to define time-varying attributes works in practice, it leads to the need for ‘dummy’ signals that have to be kept around explicitly. The big problem with this solution is that it’s a potential source of programmer error. We believe that it is just one manifestation of the more general issue that Elerea provides no way to define the death of signals in a deterministic way. Currently we rely on the garbage collector to clean up all the update activity that’s not needed any more, and it’s up to the programmer to define the signals in a way that they stop their activity at the right time.

While FRP research solved the problem of start times in several ways, it’s not nearly as clear how to describe the endpoint of a signal’s life. Most likely all we need is a few additional primitives that capture the essence of end times the same way the SignalGen monad captures the essence of start times. Recently there have been some interesting developments in this area; we’re hoping that e.g. the work of Wolfgang Jeltsch or Heinrich Apfelmus will help us come up with a practical solution.

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3 responses to “Using Bullet physics with an FRP approach (part 2)

  1. José Pedro Magalhães November 30, 2012 at 10:24 am

    Not related to the actual technical content of your post, but may I ask how you get Haskell syntax highlighting in your code examples?

  2. Pingback: Using Bullet physics with an FRP approach (part 3) « LambdaCube 3D

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