Tag Archives: modeling

Building models in Mujoco

Mujoco is an awesome simulation tool. You’re probably familiar with it from it’s use in the OpenAI gym, or from it featuring in articles and videos on model predictive control and robots learning to walk research. If you’re not familiar with it, you should check it out! Mujoco provides super fast dynamics simulation with a focus on contact dynamics. It’s especially useful for simulating robotic arms and gripping tasks.

There is a growing model repository, but it’s not unlikely you’re going to want to build your own model. It turns out this is relatively easy in Mujoco. There are two parts to defining a model: 1) The STL files, which are 3D models of the robot components, and 2) the XML file, which specifies the kinematic and dynamic relationships in the model. For STL manipulation, we use the program SketchUp because it is freely available with both offline and online versions.

This post will start by going over some settings in SketchUp assuming you haven’t used it before. We’ll then walk through how to set up the STL files for the individual components of the robot model (assuming that you already have the 3D modeling completed), steps for generating a Mujoco XML robot description file, and go over a couple of the more common problems that come up in the process.

If you don’t have the 3D modeling aleady completed, it’s worth doing a quick search around online to see if someone else has already made it because there are a lot out there not posted officially on any model repo. Github is a good source, and so is https://www.traceparts.com/en. We’re not going to cover any 3D modeling how-to here.

Shout out to my colleague Pawel Jaworski who worked through this process and wrote up the first of this document!

Sketchup Settings – Set model units on import

Before opening your STL, in the open file window select your file and click on the Options button next to Import. Select the units that the model was defined in. If you import your object and cannot see it, it is likely that the units selected during the import were incorrect and the object is simply too small to see.

Skechup Settings – Set model units for export

Mujoco uses the units specified in your STL models. ABR Control uses metres, and things generally are easiest when everyone is using the same units. To change your units to meters, go to Model Info > Length Units > 0.0m.

Sketchup Settings – Measurement precision

Before doing any measuring in SketchUp, change your measurement precision in Model Info > Units. precision to the largest number of digits to make sure you get accurate measurements (which you will need when building the Mujoco XML), the default in the online version is 1 significant digit.

Sketchup Settings – Disable snapping

If you are like me then auto-snapping is often a huge pet peeve. To disable it, go to Model Info > Units and unclick Length snapping and Angle snapping.

Sketchup Settings – Xray mode

It can be helpful to set the default view to xray mode so you can see inside the model when manipulating the components. To do this go to Styles > Default Styles > X-ray on the right hand side.


Generating component STL files

A common starting point for modeling is that you have a 3D model of the full robot. When this is the case, the first step in building a Mujoco model is to generate separate STL files for each of the components of the robot that you want to be able to move independently. For each of these component STL files, we want the point where it connects to the joint to be at the origin (0, 0, 0). We’ve found that this streamlines the process of building the model in the XML and makes it much easier to correctly specify the inertial properties.

If your 3D model is already broken up into each dynamic component (i.e. each arm section that moves independently) then you can skip to the section on centering at the origin.

Exporting components to separate STLs

Make sure you have the model unlocked. To do so, choose the Select tool and highlight the entire model. If the outline is red, it is locked. To unlock it, right click and choose Unlock.

LockedUnlocked
Unlocking the UR5 model in SketchUp

To get each component piece: Select the entire arm, deselect your component with Ctrl + Shift + Click, and then delete the rest. Go to the folder icon in the top left and select Export > STL. When exporting your model, make sure to not save your model in ASCII format, choose Binary. Repeat this until you have exported every part as its own STL.

How to save an individual component from a complete robot model

Positioning your object at the origin

For each component, identify where it will connect to the previous component, i.e. where the joint will attach it to the rest of the robot. We want to set up the STL such that this point is at the origin. By doing this we simplify building the XML file later. For the base of the robot this would simply be the bottom of the link, centered.

The easiest way to move the object to the origin is to click a point on the object with the move tool, type [0, 0, 0], and hit enter. This will move the selected point to the origin.

If your object is asymmetrical or you want to center on a point that is difficult to select with your mouse, you can use the measurement tool to set some guidelines. Setting two intersecting lines can help greatly in finding the center of an object (see dotted lines in figure).

Using dotted black guidelines to select an off-center point

Measure the offset distance for the next link

Once the component is appropriately placed at the origin, the next thing we need is the distance to the next attachment point from the origin.

The screenshots below show an example of drawing a verticle guideline to make selecting the next attachment point easy.

Making a verticle reference line that extends beyond the point of interest
Selecting the attachment point of the next component to measure the offset distance

When the next attachment point is selected, the Measurements box will display its coordinates. We will need to know all of the offset distances for each component for building the XML file, so make sure you take note!

Some measuring tips

  • Press and hold a directional button while measuring to lock an axis. The up arrow locks the z (blue) axis, the left arrow locks the y (green) axis, and the right arrow locks the x (red) axis.
  • The measuring tape line changes color to match an axis colors when it is parallel to any axis.
  • Notes on how to use the Tape Measure tool effectively

Building your model XML

The easiet way to put together your robot is to build your model up one joint and component at a time, running the simulation to make sure that it works as expected, and then adding the next piece. You will be tempted to assemble a bunch of the model all at once, but this is the siren’s song. You must be strong and resist!

For testing, we created the basic script below, which uses the ABR Control library. To check alignment you can press the space bar to pause the simulation, if you want to make the arm to move then you can change the script to send in some small value instead of zeros.

import mujoco_py as mjp
import glfw
import numpy as np

from abr_control.interfaces.mujoco import Mujoco
from abr_control.arms.mujoco_config import MujocoConfig

robot_config = MujocoConfig(xml_file='example.xml', folder='.')
interface = Mujoco(robot_config, dt=0.001)
interface.connect()

try:
    while True:
        if interface.viewer.exit:
            glfw.destroy_window(interface.viewer.window)
            break

        interface.send_forces(np.zeros(robot_config.N_JOINTS))

finally:
    interface.disconnect()

To run this script with your model, place it in the same directory as your XML file, with a folder called ‘meshes’ that has all of your STLs. If you call your XML file something other than ‘example.xml’ be sure to change that in the script above.

XML model parts

When you’re building your XML file, you’re going to want to have the Mujoco XML Reference constantly open. It’s super detailed and thorough. You can also check out this basic template file on my GitHub which shows the different parts of the file (you will need to fill this out with your own STLs and measurements).

Inside the standard definition tags, the main parts that we’ll be using are:

  • <asset> – where you import your STLs using the <mesh> tag
  • <worldbody> – where all of the objects in the simulation are defined using <body> tags, including lights, the floor, and of course your robot
  • <acutator> – where you specify which joints defined in your robot can be actuated. The ordering of these joints is important, it should be the same as their order in the kinematic tree (i.e. start with joint0 up to the last joint.)

Body building

The meat of the file is of course in the sequence of <body> tags that define the robot model. Each section of the robot looks like this:

<body name="link_n" pos="0 0 0">
    <joint name="joint_n-1" axis="0 0 0" pos="0 0 0"/>
    <geom name="link_n" type="mesh" mesh="shoulder" pos="0 0 0" euler="0 0 0"/>
    <inertial pos="0 0 0" mass="0.01" diaginertia="0 0 0"/>

    <!-- nest the next joint here -->
</body>

Set body position and geoms
Set the offset from the previous body in the pos attribute on the <body> tag, instead of on the geoms. On the <joint> you can then set pos="0 0 0" which we’ve found to help simplify debugging later on.

In each body section you can have several <joint>s and <geom>s. Geoms defined on the same body will be fused together. If you have specific inertia properties for several geoms that are fused together, you will have to create a <body> for each to be able to instantiate their own <inertial> tag. Otherwise, it’s recommended to put them all in the same <body> to optimize simulation speed.

Orientation and inertia
You may need to rotate your STLs to align them properly with the rest of the robot as you build. You can do this either in the <body> or <geom> tags. If you are using an <inertial> tag for this body, we recommend you do this using the euler parameter inside the <geom> tag, instead of inside the <body> tag. If you specify rotation in the <body> tag, you also need to apply the same rotation to your <inertia> parameters, which complicates things.

If you don’t provide an <inertial> tag, the inertia properties will be inferred from the geom.

Contype and conaffinity
If you have geoms that you don’t want to collide with other parts in the model, you can set the contype and conaffinity parameters on the geom tags. This can be handy if you have a tightly-fit 3D model an run into issues with friction, we do this on the UR5 model in the ABR Control library.

End-effector tag for ABR Control
If you’re going to use the ABR Control library operational space controller, you’ll need to add a tag <body name="EE" pos="0 0 0"> at the point of the robot that you want to control. Usually the hand.

Once you’ve added on your robot body segment, save the XML and run the above python script to test out the set up. You will likely need to do some fine tuning by iteratively adjusting the parameters and viewing the model in Mujoco. We’ve found commenting out all of the joints except the joint of interest in the XML file makes assessment much easier.

Template XML file

<mujoco model="example">
    <!-- set some defaults for units and lighting -->
    <compiler angle="radian" meshdir="meshes"/>

    <!-- import our stl files -->
    <asset>
        <mesh file="base.STL" />
        <mesh file="link1.STL" />
        <mesh file="link2.STL" />
    </asset>

    <!-- define our robot model -->
    <worldbody>
        <!-- set up a light pointing down on the robot -->
        <light directional="true" pos="-0.5 0.5 3" dir="0 0 -1" />
        <!-- add a floor so we don't stare off into the abyss -->
        <geom name="floor" pos="0 0 0" size="1 1 1" type="plane" rgba="1 0.83 0.61 0.5"/>
        <!-- the ABR Control Mujoco interface expects a hand mocap -->
        <body name="hand" pos="0 0 0" mocap="true">
            <geom type="box" size=".01 .02 .03" rgba="0 .9 0 .5" contype="2"/>
        </body>

        <!-- start building our model -->
        <body name="base" pos="0 0 0">
            <geom name="link0" type="mesh" mesh="base" pos="0 0 0"/>
            <inertial pos="0 0 0" mass="0" diaginertia="0 0 0"/>

            <!-- nest each child piece inside the parent body tags -->
            <body name="link1" pos="0 0 1">
                <!-- this joint connects link1 to the base -->
                <joint name="joint0" axis="0 0 1" pos="0 0 0"/>

                <geom name="link1" type="mesh" mesh="link1" pos="0 0 0" euler="0 3.14 0"/>
                <inertial pos="0 0 0" mass="0.75" diaginertia="1 1 1"/>

                <body name="link2" pos="0 0 1">
                    <!-- this joint connects link2 to link1 -->
                    <joint name="joint1" axis="0 0 1" pos="0 0 0"/>

                    <geom name="link2" type="mesh" mesh="link2" pos="0 0 0" euler="0 3.14 0"/>
                    <inertial pos="0 0 0" mass="0.75" diaginertia="1 1 1"/>

                    <!-- the ABR Control Mujoco interface uses the EE body to -->
                    <!-- identify the end-effector point to control with OSC-->
                    <body name="EE" pos="0 0.2 0.2">
                        <inertial pos="0 0 0" mass="0" diaginertia="0 0 0" />
                    </body>
                </body>
            </body>
        </body>
    </worldbody>

    <!-- attach actuators to joints -->
    <actuator>
        <motor name="joint0_motor" joint="joint0"/>
        <motor name="joint1_motor" joint="joint1"/>
    </actuator>

</mujoco>

If you want to use the above template XML file with the example Mujoco simulation script withot having to create your own STLs, you can download the meshes folder from ABR Control’s Jaco 2 model to get going. The placement of things is wildly inaccurate but it’ll get you going. You can also checkout the abr_control/arms/jaco2/jaco2.xml and abr_control/arms/ur5/ur5.xml files for more examples.

Hopefully this is enough to get you started, if you run into questions you can post below or on the Mujoco forums. Happy modeling!


Troubleshooting

SketchUp – I’m importing my model but I can’t see it

It is likely that the units selected during the import were incorrect and the object is simply too small to see. When opening your STL, in the open file window select your file and click on the options button next to Import to change the units.

Mujoco – My arm isn’t moving, or moves slightly and stops

In this case you likely have collisions between links. You can either add a small gap between links (make sure to account for this shift in subsequent links and joints), or you can use the contype and conaffinity tags to set up the model such that collisions between the two components are not calculated.

For example, in our UR5 model, we set the touching geoms between links to have different contype and conaffinity values so that they don’t scrape against one another and prevent movement.

Mujoco – Parts of my model are spinning around wildly

This usually arises from being instantiated in contact with another part of the model. Sometimes it looks like there is clearly no contact between different model segments, but in fact there is because of how the contact dynamics are calculated.

Only convex shapes are supported. To see the shapes being used to calculate the contact dynamics, press F1 while the model is running. 

There is no space between the neck and jaw. To address this, you need break things up into multiple component STL files and then stitch them together in the XML. For example, in the above skeleton you would need to break it up into skull and spine STLs. NB: I will not be answering any questions about why a skeleton was used in this example.

Tagged , , , , ,

Building a spiking neural model of adaptive arm control

About a year ago I published the work from my thesis in a paper called ‘A spiking neural model of adaptive arm control’. In this paper I presented the Recurrent Error-driven Adaptive Control Hierarchy (REACH) model. The goal of the model is to begin working towards reproducing behavioural level phenomena of human movement with biologically plausible spiking neural networks.

To do this, I start by using three methods from control literature (operational space control, dynamic movement primitives, and non-linear adaptive control) to create an algorithms level model of the motor control system that captures behavioural level phenomena of human movement. Then I explore how this functionality could be mapped to the primate brain and implemented in spiking neurons. Finally, I look at the data generated by this model on the behavioural level (e.g. kinematics of movement), the systems level (i.e. analysis of populations of neurons), and the single-cell level (e.g. correlating neural activity with movement parameters) and compare/contrast with experimental data.

By having a full model framework (from observable behaviour to neural spikes) is to have a more constrained computational model of the motor control system; adding lower-level biological constraints to behavioural models and higher-level behavioural constraints to neural models.

In general, once you have a model, the critical next step is to generating testable predictions that can be used to discriminate between other models with different implementations or underlying algorithms. Discriminative predictions allow us to design experiments that can gather evidence in favour or against different hypotheses of brain function, and provide clues to useful directions for further research. Which is the real contribution of computational modeling.

So that’s a quick overview of the paper; there are quite a few pages of supplementary information that describe the details of the model implementation, and I provided the code and data used to generate the data analysis figures. However, code to explicitly run the model on your own has been missing. As one of the major points of this blog is to provide code for furthering research, this is pretty embarrassing. So, to begin to remedy this, in this post I’m going to work through a REACH framework for building models to control a two-link arm through reaching in a line, tracing a circle, performing the centre-out reaching task, and adapting online to unexpected perturbations during reaching imposed by a joint-velocity based forcefield.

This post is directed towards those who have already read the paper (although not necessarily the supplementary material). To run the scripts you’ll need Nengo, Nengo GUI, and NengoLib all installed. There’s a description of the theory behind the Neural Engineering Framework, which I use extensively in my Nengo modeling, in the paper. I’m hoping that between that and code readability / my explanations below that most will be comfortable starting to play around with the code. But if you’re not, and would like more resources, you can check out the Getting Started page on the Nengo website, the tutorials from How To Build a Brain, and the examples in the Nengo GUI.

You can find all of the code up on my GitHub.

NOTE: I’m using the two-link arm (which is fully implemented in Python) instead of the three-link arm (which has compile issues for Macs) both so that everyone should be able to run the model arm code and to reduce the number of neurons that are required for control, so that hopefully you can run this on you laptop in the event that you don’t have a super power Ubuntu work station. Scaling this model up to the three-link arm is straight-forward though, and I will work on getting code up (for the three-link arm for non-Mac users) as a next project.

Implementing PMC – the trajectory generation system

I’ve talked at length about dynamic movement primitives (DMPs) in previous posts, so I won’t describe those again here. Instead I will focus on their implementation in neurons.

def generate(y_des, speed=1, alpha=1000.0):
    beta = alpha / 4.0

    # generate the forcing function
    forces, _, goals = forcing_functions.generate(
        y_des=y_des, rhythmic=False, alpha=alpha, beta=beta)

    # create alpha synapse, which has point attractor dynamics
    tau = np.sqrt(1.0 / (alpha*beta))
    alpha_synapse = nengolib.Alpha(tau)

    net = nengo.Network('PMC')
    with net:
        net.output = nengo.Node(size_in=2)

        # create a start / stop movement signal
        time_func = lambda t: min(max(
            (t * speed) % 4.5 - 2.5, -1), 1)

        def goal_func(t):
            t = time_func(t)
            if t <= -1:
                return goals[0]
            return goals[1]
        net.goal = nengo.Node(output=goal_func, label='goal')

        # -------------------- Ramp ---------------------------
        ramp_node = nengo.Node(output=time_func, label='ramp')
        net.ramp = nengo.Ensemble(
            n_neurons=500, dimensions=1, label='ramp ens')
        nengo.Connection(ramp_node, net.ramp)

        # ------------------- Forcing Functions ---------------
        def relay_func(t, x):
            t = time_func(t)
            if t <= -1:
                return [0, 0]
            return x
        # the relay prevents forces from being sent on reset
        relay = nengo.Node(output=relay_func, size_in=2)

        domain = np.linspace(-1, 1, len(forces[0]))
        x_func = interpolate.interp1d(domain, forces[0])
        y_func = interpolate.interp1d(domain, forces[1])
        transform=1.0/alpha/beta
        nengo.Connection(net.ramp, relay[0], transform=transform,
                         function=x_func, synapse=alpha_synapse)
        nengo.Connection(net.ramp, relay[1], transform=transform,
                         function=y_func, synapse=alpha_synapse)
        nengo.Connection(relay, net.output)

        nengo.Connection(net.goal[0], net.output[0],
                         synapse=alpha_synapse)
        nengo.Connection(net.goal[1], net.output[1],
                         synapse=alpha_synapse)
    return net

The generate method for the PMC takes in a desired trajectory, y_des, as a parameter. The first thing we do (on lines 5-6) is calculate the forcing function that will push the DMP point attractor along the desired trajectory.

The next thing (on lines 9-10) is creating an Alpha (second-order low-pass filter) synapse. By writing out the dynamics of a point attractor in Laplace space, one of the lab members, Aaron Voelker, noticed that the dynamics could be fully implemented by creating an Alpha synapse with an appropriate choice of tau. I walk through all of the math behind this in this post. Here we’ll use that more compact method and project directly to the output node, which improves performance and reduces the number of neurons.

Inside the PMC network we create a time_func node, which is the pace-setter during simulation. It will output a linear ramp from -1 to 1 every few seconds, with the pace set by the speed parameter, and then pause before restarting.

We also have a goal node, which will provide a target starting and ending point for the trajectory. Both the time_func and goal nodes are created and used as a model simulation convenience, and proposed to be generated elsewhere in the brain (the basal ganglia, why not? #igotreasons #provemewrong).

The ramp ensemble is the most important component of the trajectory generation system. It takes the output from the time_func node as input, and generates the forcing function which will guide our little system through the trajectory that was passed in. The ensemble itself is nothing special, but rather the function that it approximates on its outgoing connection. We set up this function approximation with the following code:

        domain = np.linspace(-1, 1, len(forces[0]))
        x_func = interpolate.interp1d(domain, forces[0])
        y_func = interpolate.interp1d(domain, forces[1])
        transform=1.0/alpha/beta
        nengo.Connection(net.ramp, relay[0], transform=transform,
                         function=x_func, synapse=alpha_synapse)
        nengo.Connection(net.ramp, relay[1], transform=transform,
                         function=y_func, synapse=alpha_synapse)
        nengo.Connection(relay, net.output)

We want the forcing function be generated as the signals represented in the ramp ensemble moves from -1 to 1. To achieve this, we create interpolation functions, x_func and y_func, which are set up to generate the forcing function values mapped to input values between -1 and 1. We pass these functions into the outgoing connections from the ramp population (one for x and one for y). So now when the ramp ensemble is representing -1, 0, and 1 the output along the two connections will be the starting, middle, and ending x and y points of the forcing function trajectory. The transform and synapse are set on each connection with the appropriate gain values and Alpha synapse, respectively, to implement point attractor dynamics.

NOTE: The above DMP implementation can generate a trajectory signal with as many dimensions as you would like, and all that’s required is adding another outgoing Connection projecting from the ramp ensemble.

The last thing in the code is hooking up the goal node to the output, which completes the point attractor implementation.

Implementing M1 – the kinematics of operational space control

In REACH, we’ve modelled the primary motor cortex (M1) as responsible for taking in a desired hand movement (i.e. target_position - current_hand_position) and calculating a set of joint torques to carry that out. Explicitly, M1 generates the kinematics component of an OSC signal:

\textbf{u}_\textrm{M1} = \textbf{J}^T \textbf{M}_\textbf{x} (k_p (\textbf{x}^* - \textbf{x}))

In the paper I did this using several populations of neurons, one to calculate the Jacobian, and then an EnsembleArray to perform the multiplication for the dot product of each dimension separately. Since then I’ve had the chance to rework things and it’s now done entirely in one ensemble.

Now, the set up for the M1 model that computes the above function is to have a single ensemble of neurons that takes in the joint angles, \textbf{q}, and control signal \textbf{u}_\textbf{x} = k_p (\textbf{x}^* - \textbf{x}), and outputs the function above. Sounds pretty simple, right? Simple is good.

Let’s look at the code (where I’ve stripped out the comments and some debugging code):

def generate(arm, kp=1, operational_space=True,
             inertia_compensation=True, means=None, scales=None):

    dim = arm.DOF + 2

    means = np.zeros(dim) if means is None else means
    scales = np.ones(dim) if scales is None else scales
    scale_down, scale_up = generate_scaling_functions(
        np.asarray(means), np.asarray(scales))

    net = nengo.Network('M1')
    with net:
        # create / connect up M1 ------------------------------
        net.M1 = nengo.Ensemble(
            n_neurons=1000, dimensions=dim,
            radius=np.sqrt(dim),
            intercepts=AreaIntercepts(
                dimensions=dim,
                base=nengo.dists.Uniform(-1, .1)))

        # expecting input in form [q, x_des]
        net.input = nengo.Node(output=scale_down, size_in=dim)
        net.output = nengo.Node(size_in=arm.DOF)

        def M1_func(x, operational_space, inertia_compensation):
            """ calculate the kinematics of the OSC signal """
            x = scale_up(x)
            q = x[:arm.DOF]
            x_des = kp * x[arm.DOF:]

            # calculate hand (dx, dy)
            if operational_space:
                J = arm.J(q=q)

                if inertia_compensation:
                    # account for inertia
                    Mx = arm.Mx(q=q)
                    x_des = np.dot(Mx, x_des)
                # transform to joint torques
                u = np.dot(J.T, x_des)
            else:
                u = x_des

                if inertia_compensation:
                    # account for mass
                    M = arm.M(q=q)
                    u = np.dot(M, x_des)

            return u

        # send in system feedback and target information
        # don't account for synapses twice
        nengo.Connection(net.input, net.M1, synapse=None)
        nengo.Connection(
            net.M1, net.output,
            function=lambda x: M1_func(
                x, operational_space, inertia_compensation),
            synapse=None)

    return net

The ensemble of neurons itself is created with a few interesting parameters:

        net.M1 = nengo.Ensemble(
            n_neurons=1000, dimensions=dim,
            radius=np.sqrt(dim),
            intercepts=AreaIntercepts(
                dimensions=dim, base=nengo.dists.Uniform(-1, .1)))

Specifically, the radius and intercepts parameters.

Setting the intercepts

First we’ll discuss setting the intercepts using the AreaIntercepts distribution. The intercepts of a neuron determine how much of state space a neuron is active in, which we’ll refer to as the ‘active proportion’. If you don’t know what kind of functions you want to approximate with your neurons, then you having the active proportions for your ensemble chosen from a uniform distribution is a good starting point. This means that you’ll have roughly the same number of neurons active across all of state space as you do neurons that are active over half of state space as you do neurons that are active over very small portions of state space.

By default, Nengo sets the intercepts such that the distribution of active proportions is uniform for lower dimensional spaces. But when you start moving into higher dimensional spaces (into a hypersphere) the default method breaks down and you get mostly neurons that are either active for all of state space or almost none of state space. The AreaIntercepts class calculates how the intercepts should be set to achieve the desire active proportion inside a hypersphere. There are a lot more details here that you can read up on in this IPython notebook by Dr. Terrence C. Stewart.

What you need to know right now is that we’re setting the intercepts of the neurons such that the percentage of state space for which any given neuron is active is chosen from a uniform distribution between 0% and 55%. In other words, a neuron will maximally be active in 55% of state space, no more. This will let us model more nonlinear functions (such as the kinematics of the OSC signal) with fewer neurons. If this description is clear as mud, I really recommend checking out the IPython notebook linked above to get an intuition for what I’m talking about.

Scaling the input signal

The other parameter we set on the M1 ensemble is the radius. The radius scales the range of input values that the ensemble can represent, which is by default anything inside the unit hypersphere (i.e. hypersphere with radius=1). If the radius is left at this default value, the neural activity will saturate for vectors with magnitude greater than 1, leading to inaccurate vector representation and function approximation for input vectors with magnitude > 1. For lower dimensions this isn’t a terrible problem, but as the dimensions of the state space you’re representing grow it becomes more common for input vectors to have a norm greater than 1. Typically, we’d like to be able to, at a minimum, represent vectors with any number of dimensions where any element can be anywhere between -1 and 1. To do this, all we have to do is calculate the norm of the unit vector size dim, which is np.sqrt(dim) (the magnitude of a vector size dim with all elements set to one).

Now that we’re able to represent vectors where the input values are all between -1 and 1, the last part of this sub-network is scaling the input to be between -1 and 1. We use two scaling functions, scale_down and scale_up. The scale_down function subtracts a mean value and scales the input signal to be between -1 and 1. The scale_up function reverts the vector back to it’s original values so that calculations can be carried out normally on the decoding. In choosing mean and scaling values, there are two ways we can set these functions up:

  1. Set them generally, based on the upper and lower bounds of the input signal. For M1, the input is [\textbf{q}, \textbf{u}_\textbf{x}] where \textbf{u}_\textbf{x} is the control signal in end-effector space, we know that the joint angles are always in the range 0 to \pi (because that’s how the arm simulation is programmed), so we can set the means and scales to be \frac{\pi}{2} for \textbf{q}. For \textbf{u} a mean of zero is reasonable, and we can choose (arbitrarily, empirically, or analytically) the largest task space control signal we want to represent accurately.
  2. Or, if we know the model will be performing a specific task, we can look at the range of input values encountered during that task and set the means and scales terms appropriately. For the task of reaching in a straight line, the arm moves in a very limited state space and we can set the mean and we can tune these parameter to be very specific:
                                 means=[0.6, 2.2, 0, 0],
                                 scales=[.25, .25, .25, .25]
    

The benefit of the second method, although one can argue it’s parameter tuning and makes things less biologically plausible, is that it lets us run simulations with fewer neurons. The first method works for all of state space, given enough neurons, but seeing as we don’t always want to be simulating 100k+ neurons we’re using the second method here. By tuning the scaling functions more specifically we’re able to run our model using 1k neurons (and could probably get away with fewer). It’s important to keep in mind though that if the arm moves outside the expected range the control will become unstable.

Implementing CB – the dynamics of operational space control

The cerebellum (CB) sub-network has two components to it: dynamics compensation and dynamics adaptation. First we’ll discuss the dynamics compensation. By which I mean the k_v \textbf{M} (\textbf{q}^* - \textbf{q}) term from the OSC signal.

Much like the calculating the kinematics term of the OSC signal in M1, we can calculate the entire dynamics compensation term using a single ensemble with an appropriate radius, scaled inputs, and well chosen intercepts.

def generate(arm, kv=1, learning_rate=None, learned_weights=None,
             means=None, scales=None):
    dim = arm.DOF * 2

    means = np.zeros(dim) if means is None else means
    scales = np.ones(dim) if scales is None else scales
    scale_down, scale_up = generate_scaling_functions(
        np.asarray(means), np.asarray(scales))

    net = nengo.Network('CB')
    with net:
        # create / connect up CB --------------------------------
        net.CB = nengo.Ensemble(
            n_neurons=1000, dimensions=dim,
            radius=np.sqrt(dim),
            intercepts=AreaIntercepts(
                dimensions=dim,
                base=nengo.dists.Uniform(-1, .1)))
        # expecting input in form [q, dq, u]
        net.input = nengo.Node(output=scale_down,
                               size_in=dim+arm.DOF+2)
        cb_input = nengo.Node(size_in=dim, label='CB input')
        nengo.Connection(net.input[:dim], cb_input)
        # output is [-Mdq, u_adapt]
        net.output = nengo.Node(size_in=arm.DOF*2)

        def CB_func(x):
            """ calculate the dynamic component of OSC signal """
            x = scale_up(x)
            q = x[:arm.DOF]
            dq = x[arm.DOF:arm.DOF*2]

            # calculate inertia matrix
            M = arm.M(q=q)
            return -np.dot(M, kv * dq)
        # connect up the input and output
        nengo.Connection(net.input[:dim], net.CB)
        nengo.Connection(net.CB, net.output[:arm.DOF],
                         function=CB_func, synapse=None)

I don’t think there’s anything noteworthy going on here, most of the relevant details have already been discussed…so we’ll move on to the adaptation!

Implementing CB – non-linear dynamics adaptation

The final part of the model is the non-linear dynamics adaptation, modelled as a separate ensemble in the cerebellar sub-network (a separate ensemble so that it’s more modular, the learning connection could also come off of the other CB population). I work through the details and proof of the learning rule in the paper, so I’m not going to discuss that here. But I will restate the learning rule here:

\dot{\textbf{d}} = \textbf{L}_d \textbf{A} \otimes \textbf{u},

where \textbf{d} are the neural decoders, \textbf{L}_d is the learning rate, \textbf{A} is the neural activity of the ensemble, and \textbf{u} is the joint space control signal sent to the arm. This is a basic delta learning rule, where the decoders of the active neurons are modified to push the decoded function in a direction that reduces the error.

The adaptive ensemble can be initialized either using saved weights (passed in with the learned_weights paramater) or as all zeros. It is important to note that setting decoders to all zeros means that does not mean having zero neural activity, so learning will not be affected by this initialization.

        # dynamics adaptation------------------------------------
        if learning_rate is not None:
            net.CB_adapt = nengo.Ensemble(
                n_neurons=1000, dimensions=arm.DOF*2,
                radius=np.sqrt(arm.DOF*2),
                # enforce spiking neurons
                neuron_type=nengo.LIF(),
                intercepts=AreaIntercepts(
                    dimensions=arm.DOF,
                    base=nengo.dists.Uniform(-.5, .2)))

            net.learn_encoders = nengo.Connection(
                net.input[:arm.DOF*2], net.CB_adapt,)

            # if no saved weights were passed in start from zero
            weights = (
                learned_weights if learned_weights is not None
                else np.zeros((arm.DOF, net.CB_adapt.n_neurons)))
            net.learn_conn = nengo.Connection(
                # connect directly to arm so that adaptive signal
                # is not included in the training signal
                net.CB_adapt.neurons, net.output[arm.DOF:],
                transform=weights,
                learning_rule_type=nengo.PES(
                    learning_rate=learning_rate),
                synapse=None)

            nengo.Connection(net.input[dim:dim+2],
                             net.learn_conn.learning_rule,
                             transform=-1, synapse=.01)
    return net

We’re able to implement that learning rule using Nengo’s prescribed error-sensitivity (PES) learning on our connection from the adaptive ensemble to the output. With this set up the system will be able to learn to adapt to perturbations that are functions of the input (set here to be [\textbf{q}, \dot{\textbf{q}}]).

The intercepts in this population are set to values I found worked well for adapting to a few different forces, but it’s definitely a parameter to play with in your own scripts if you’re finding that there’s too much or not enough generalization of the decoded function output across the state space.

One other thing to mention is that we need to have a relay node to amalgamate the control signals output from M1 and the dynamics compensation ensemble in the CB. This signal is used to train the adaptive ensemble, and it’s important that the adaptive ensemble’s output is not included in the training signal, or else the system quickly goes off to positive or negative infinity.

Implementing S1 – a placeholder

The last sub-network in the REACH model is a placeholder for a primary sensory cortex (S1) model. This is just a set of ensembles that represents the feedback from the arm and relay it on to the rest of the model.

def generate(arm, direct_mode=False, means=None, scales=None):
    dim = arm.DOF*2 + 2  # represents [q, dq, hand_xy]

    means = np.zeros(dim) if means is None else means
    scales = np.ones(dim) if scales is None else scales
    scale_down, scale_up = generate_scaling_functions(
        np.asarray(means), np.asarray(scales))

    net = nengo.Network('S1')
    with net:
        # create / connect up S1 --------------------------------
        net.S1 = nengo.networks.EnsembleArray(
            n_neurons=50, n_ensembles=dim)

        # expecting input in form [q, x_des]
        net.input = nengo.Node(output=scale_down, size_in=dim)
        net.output = nengo.Node(
            lambda t, x: scale_up(x), size_in=dim)

        # send in system feedback and target information
        # don't account for synapses twice
        nengo.Connection(net.input, net.S1.input, synapse=None)
        nengo.Connection(net.S1.output, net.output, synapse=None)

    return net

Since there’s no function that we’re decoding off of the represented variables we can use separate ensembles to represent each dimension with an EnsembleArray. If we were going to decode some function of, for example, q0 and dq0, then we would need an ensemble that represents both variables. But since we’re just decoding out f(x) = x, using an EnsembleArray is a convenient way to decrease the number of neurons needed to accurately represent the input.

Creating a model using the framework

The REACH model has been set up to be as much of a plug and play system as possible. To generate a model you first create the M1, PMC, CB, and S1 networks, and then they’re all hooked up to each other using the framework.py file. Here’s an example script that controls the arm to trace a circle:

def generate():
    kp = 200
    kv = np.sqrt(kp) * 1.5

    center = np.array([0, 1.25])
    arm_sim = arm.Arm2Link(dt=1e-3)
    # set the initial position of the arm
    arm_sim.init_q = arm_sim.inv_kinematics(center)
    arm_sim.reset()

    net = nengo.Network(seed=0)
    with net:
        net.dim = arm_sim.DOF
        net.arm_node = arm_sim.create_nengo_node()
        net.error = nengo.Ensemble(1000, 2)
        net.xy = nengo.Node(size_in=2)

        # create an M1 model -------------------------------------
        net.M1 = M1.generate(arm_sim, kp=kp,
                             operational_space=True,
                             inertia_compensation=True,
                             means=[0.6, 2.2, 0, 0],
                             scales=[.5, .5, .25, .25])

        # create an S1 model -------------------------------------
        net.S1 = S1.generate(arm_sim,
                             means=[.6, 2.2, -.5, 0, 0, 1.25],
                             scales=[.5, .5, 1.7, 1.5, .75, .75])

        # subtract current position to get task space direction
        nengo.Connection(net.S1.output[net.dim*2:], net.error,
                         transform=-1)

        # create a trajectory for the hand to follow -------------
        x = np.linspace(0.0, 2.0*np.pi, 100)
        PMC_trajectory = np.vstack([np.cos(x) * .5,
                                    np.sin(x) * .5])
        PMC_trajectory += center[:, None]
        # create / connect up PMC --------------------------------
        net.PMC = PMC.generate(PMC_trajectory, speed=1)
        # send target for calculating control signal
        nengo.Connection(net.PMC.output, net.error)
        # send target (x,y) for plotting
        nengo.Connection(net.PMC.output, net.xy)

        net.CB = CB.generate(arm_sim, kv=kv,
                             means=[0.6, 2.2, -.5, 0],
                             scales=[.5, .5, 1.6, 1.5])

    model = framework.generate(net=net, probes_on=True)
    return model

In line 50 you can see the call to the framework code, which will hook up the most common connections that don’t vary between the different scripts.

The REACH model has assigned functionality to each area / sub-network, and you can see the expected input / output in the comments at the top of each sub-network file, but the implementations are open. You can create your own M1, PMC, CB, or S1 sub-networks and try them out in the context of a full model that generates high-level movement behaviour.

Running the model

To run the model you’ll need Nengo, Nengo GUI, and NengoLib all installed. You can then pull open Nengo GUI and load any of the a# scripts. In all of these scripts the S1 model is just an ensemble that represents the output from the arm_node. Here’s what each of the scripts does:

  1. a01 has a spiking M1 and CB, dynamics adaptation turned off. The model guides the arm in reaching in a straight line to a single target and back.
  2. a02 has a spiking M1, PMC, and CB, dynamics adaptation turned off. The PMC generates a path for the hand to follow that traces out a circle.
  3. a03 has a spiking M1, PMC, and CB, dynamics adaptation turned off. The PMC generates a path for the joints to follow, which moves the hand in a straight line to a target and back.
  4. a04 has a spiking M1 and CB, dynamics adaptation turned off. The model performs the centre-out reaching task, starting at a central point and reaching to 8 points around a circle.
  5. a05 has a spiking M1 and CB, and dynamics adaptation turned on. The model performs the centre-out reaching task, starting at a central point and reaching to 8 points around a circle. As the model reaches, a forcefield is applied based on the joint velocities that pushes the arm as it tries to reach the target. After 100-150 seconds of simulation the arm has adapted and learned to reach in a straight line again.

Here’s what it looks like when you pull open a02 in Nengo GUI:

REACH_a02

I’m not going to win any awards for arm animation, but! It’s still a useful visualization, and if anyone is proficient in javascript and want’s to improve it, please do! You can see the network architecture in the top left, the spikes generated by M1 and CB to the right of that, the arm in the bottom left, and the path traced out by the hand just to the right of that. On the top right you can see the a02 script code, and below that the Nengo console.

Conclusions

One of the most immediate extensions (aside from any sort of model of S1) that comes to mind here is implementing a more detailed cerebellar model, as there are several anatomically detailed models which have the same supervised learner functionality (for example (Yamazaki and Nagao, 2012)).

Hopefully people find this post and the code useful for their own work, or at least interesting! In the ideal scenario this would be a community project, where researchers add models of different brain areas and we end up with a large library of implementations to build larger models with in a Mr. Potato Head kind of fashion.

You can find all of the code up on my GitHub. And again, this code all should have been publicly available along with the publication. Hopefully the code proves useful! If you have any questions about it please don’t hesitate to make a comment here or contact me through email, and I’ll get back to you as soon as I can.

Tagged , , , , , ,

Nengo scripting: absolute value

To just get the code you can copy / paste from below or get the code from my github: absolute_value.py

This is a simple script for performing the absolute value function in Nengo. The most efficient way I’ve found to implement this is to use two separate populations for each dimension of the input signal, one to represent the signal when it’s greater than zero and simply relay it to the output node, and one to represent the signal when it’s less than zero, and project x * -1 to the output node. Here’s the code, and I’ll step through it below.

def make_abs_val(name, neurons, dimensions, intercept=[0]):
    def mult_neg_one(x):
        return x[0] * -1 

    abs_val = nef.Network(name)

    abs_val.make('input', neurons=1, dimensions=dimensions, mode='direct') # create input relay
    abs_val.make('output', neurons=1, dimensions=dimensions, mode='direct') # create output relay
    
    for d in range(dimensions): # create a positive and negative population for each dimension in the input signal
        abs_val.make('abs_pos%d'%d, neurons=neurons, dimensions=1, encoders=[[1]], intercept=intercept)
        abs_val.make('abs_neg%d'%d, neurons=neurons, dimensions=1, encoders=[[-1]], intercept=intercept)

        abs_val.connect('input', 'abs_pos%d'%d, index_pre=d)
        abs_val.connect('input', 'abs_neg%d'%d, index_pre=d)
    
        abs_val.connect('abs_pos%d'%d, 'output', index_post=d)
        abs_val.connect('abs_neg%d'%d, 'output', index_post=d, func=mult_neg_one)

    return abs_val.network

First off, the function takes in parameters specifying the number of dimensions, the number of neurons for each population generated, a name, and optionally an intercept value. I’ll come back to why the intercept value is an option in a bit.

Inside the make_abs_val function another function that multiplies the first dimension of its input by -1 is specified. This mult_neg_one function is going to be used by our population representing negative values of the input signal.

Next, we create the network and call it abs_val. Input and output relay nodes are then created, with one neuron, of the specified dimension number, and the populations are set to direct mode. These are the populations that will be connected to from populations outside of the abs_val network.

Now there is a loop for each dimension of the input signal. Inside, two populations are created, where the only difference is their encoder values. Their intercepts specify the start of the range of values they represent. The default is 0, so when it’s not specified these populations will represent values from 0 to 1 (1 is the default end value of the range). For abs_neg, the encoders=[[-1]] line changes the range of values represented from (0,1) to (-1,0). Now we have two populations for dimension d, one that represents only positive values (between 0 and 1), and one that represents only negative values (between -1 and 0). And we’re almost done!

The only thing left to do is to hook up the populations to the input and output appropriately and incorporate the mult_neg_one function into the connection between each of the abs_neg populations and the output relay node. We want each set of populations representing a single dimension to receive and project back into the appropriate dimension of the output relay function, so we employ the index_pre and index_post parameters. Because we want each set to receive only dimension d from the input, on that connection specification we set index_pre=d. When setting up the projections to the output relay node, we similarly only want each population to project to the appropriate output dimension d, so we set index_post=d.

By default, the connect call sets up a communication channel, that is to say no computation is performed on the signal passed from the pre to the post population. This is what we want for abs_pos population, but for the abs_neg population we want the mult_neg_one function to be applied, so that any negative values are multiplied by -1, and give us positive values. This can be done by using the func parameter, and so we call it and set it func=mult_neg_one. Now the connection from abs_neg to the output node will be transformed by the mult_neg_one function.

And that’s it! Here is a script that gets it running (which can also be found on my github: absolute_value.py):

import nef
import random

# constants / parameter setup etc
N = 50 # number of neurons
D = 3 # number of dimensions

def make_abs_val(name, neurons, dimensions, intercept=[0]):
    def mult_neg_one(x):
        return x[0] * -1 

    abs_val = nef.Network(name)

    abs_val.make('input', neurons=1, dimensions=dimensions, mode='direct') # create input relay
    abs_val.make('output', neurons=1, dimensions=dimensions, mode='direct') # create output relay
    
    for d in range(dimensions): # create a positive and negative population for each dimension in the input signal
        abs_val.make('abs_pos%d'%d, neurons=neurons, dimensions=1, encoders=[[1]], intercept=intercept)
        abs_val.make('abs_neg%d'%d, neurons=neurons, dimensions=1, encoders=[[-1]], intercept=intercept)

        abs_val.connect('input', 'abs_pos%d'%d, index_pre=d)
        abs_val.connect('input', 'abs_neg%d'%d, index_pre=d)
    
        abs_val.connect('abs_pos%d'%d, 'output', index_post=d)
        abs_val.connect('abs_neg%d'%d, 'output', index_post=d, func=mult_neg_one)

    return abs_val.network

net = nef.Network('network')

# Create absolute value subnetwork and add it to net
net.add(make_abs_val(name='abs_val', dimensions=D, neurons=N))

# Create function input
net.make_input('input', values=[random.random() for d in range(D)])

# Connect things up
net.connect('input', 'abs_val.input')

# Add it all to the Nengo world
net.add_to_nengo()

And here’s a picture of it running.

Tagged , , ,