Skip to content

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
Various demos from the Berkeley RLL
Python C++ JavaScript Common Lisp Java C Other
tree: 7ee868e519

Fetching latest commit…

Cannot retrieve the latest commit at this time

Failed to load latest commit information.
dryer_demo
omni_teleop
socks_icra
unfolding_smach
.gitignore
CMakeLists.txt
Makefile
README
folding-setup.bash
stack.xml

README

                                                                     
                                                                     
                                                                     
                                             
Our code is laid out in five stacks. In order to use it, please check out the following packages:

for pkg in berkeley_demos berkeley_utils folding pr2_simple_motions visual_feedback; 
do svn co svn+ssh://svn@rll.berkeley.edu/main/berkeley-ros-pkg/stacks/$pkg/tags/willow/$pkg; 
done

~~~~~~~~~~~~~~~~~~~~~~
pr2_simple_motions
~~~~~~~~~~~~~~~~~~~~~~

Brief:
Contains a number of packages for moving the PR2 with ease. Currently assumes you are running CTurtle -- it looks like
one dependency (pr2_2dnav_slam) is having trouble running Diamondback, though we may circumvent that somehow.

Packages:

    ----------------------
    pr2_simple_arm_motions
    ----------------------
    
    Brief: Provides a framework for moving the arms. Particularly meant for manipulating nonrigid objects.

    Scripts/Nodes:
    
    move_gripper.py:      Provides services for moving the gripper
    grip_watcher.py:      "Watches" the gripper to determine whether or not it is holding cloth. Does not require touch sensors.
    gripper_tip_frame.py: Broadcasts a "tip" frame which we find more useful than the tool frame for our tasks. 
    table_frame.py:       Broadcasts a frame for the table, assuming we are operating on one. Requires a global parameter "table_height" to be set.

    Src/Include Files:        

    GripUtils.py:         Simple functions for moving the arm, grabbing objects, etc. Used very frequently in our code.
    ArmMotionStates.py:   Some common arm-related Smach states.

    Launch Files:

    move_arms.launch:     Launches the arm controllers + move_gripper services

    ------------------------------------
    pr2_simple_[head/torso/base]_motions
    ------------------------------------

    Brief: Same idea as before for various other parts of the robot. Less interesting and probably self-explanatory.

    ------------------------
    pr2_arm_move_ik_synced
    ------------------------

    Brief: Modeled directly after pr2_arm_move_ik, but adds synchronization so one arm doesn't outrun the other.

    Source Files: See original pr2_arm_move_ik

    ------------------------------
    pr2_simple_motions_[msgs/srvs]
    ------------------------------

    Brief: Hosts the messages and services we use to move the robot. Again, should be self explanatory.

    ------------------------
    pr2_simple_motions_apps
    ------------------------

    Brief: Combines the packages to allow us to move the whole robot. The particular contribution, aside
    from basic launch files, is the addition of Stance and Primitive servers, which let us save the
    instantaneous position of the robot's arms/head/torso/base in Cartesian space rather than joint space,
    and recall them later.

    Scripts/Nodes:
    
    stance_server.py:     Initiates a server which can save the instantaneous pose of the robot to disk and provide services to call to move to said pose
    primitive_server.py:  Initiates a server which can save timed sequences of multiple stances to disk and provide services to call to execute said sequence
    location_server.py:   Initiates a server which can save locations in the map frame and provide services to go to said location
    (Not sure about the rest: I've never seen them used, and will delete unless an undergrad claims it)

    Launch Files:

    move_all.launch:               Initiates control of the entire PR2
    stances_and_primitives.launch  Example launch file for stance & primitive servers (see berkeley_demos/*/launch/stances.launch for more examples)


~~~~~~~~
folding
~~~~~~~~

Brief:
Contains all packages relevant to executing folds in fabric

Packages:

    ----------------
    folding_geometry
    ----------------

    Brief: Handles the 2D geometry of folding, as well as how to convert from 3D cloth points to this representation

    Scripts/Nodes:

    generate_poly_from_points.py: Takes in a stream of 3D points, outputs a "polygon". Was more relevant when we weren't assuming a flat table.
    poly_gui_bridge.py:           Takes a 3D polygon as input, and interfaces with the FoldingGUI window to display the polygon and track folds/etc in it. Also publishes output fold trajectories for others nodes (such as the fold_executor) to use.

    Src/Include Files:
    
    FoldingGUI.py: Extends the ShapeWindow.py framework to allow us to draw and fold arbitrary polygons. Also contains primitive fold sequences for shirts, sweaters, pants, and towels.

    -----------------
    folding_execution
    -----------------

    Brief: Making the robot actually fold

    Scripts/Nodes:

    fold_executor.py: Takes a 3D fold specification as input and executes the folding maneuver with the PR2.

    -----------------
    folding_vision
    -----------------

    Brief: Provides vision to the fold procedure (added later, hence the strange modularity)

    Scripts/Nodes:

    vision_bridge.py: Interfaces with our vision systems to update the 2D representation of the cloth polygon we are folding, while we fold it.

    Launch Files:

    vision.launch: Launches the necessary vision nodes for the folding task

    --------------
    folding_apps
    ---------------

    Brief: Meant to join all folding together. At the moment it only consists of launch files.

    Launch Files:

    gui_interface.launch: Launches the interface. Will require you to call vision_bridge_node/locate_polygon to start the detection/folding process.

~~~~~~~~~~~~~~~
visual_feedback
~~~~~~~~~~~~~~~

Brief:
Where all the actual computer vision is done.

Packages:

    ----------------
    image_processor
    ----------------

    Brief: The core package for us to run a vision system on the robot. Provides, in effect, a wrapper which handles any 3D information (stereo or intersection with a known table)
    and data output. All that is required of us is to fill in one function: process, which takes a 2D image as input and outputs points, parameters, and an optional annotated image.

    Scripts/Nodes:

    image_processor_node: The basic node which all others inherit from
    click_node.py: A good human-in-the-loop "vision" system for debugging: when presented with a camera image, pops up with a window for us to click on points on the image manually.
    clump_center_node.py: Returns the center of an object on a green background
    furthest_corner_node.py: Returns the furthest corner (left or right) of the article of cloth on the table
    shape_fitter_node.py: Fits a shape model (see Miller ICRA2011) to the object in the image. Outputs a set of landmark points for the robot to work with.
    triangle_fitter_node.py: A special case of the shape model which uses additional appearance information to determine which half of the towel is the
    front and back when it is held by two opposite corners.

    ---------------------
    visual_feedback_utils
    ---------------------
     Brief: Contains utility functions for a lot of shared vision tasks.

     Scripts/Nodes:
     annotator.py: Used to annotate points on images and provide ground truth to compare results to
     

     Src/Include Files:

     shape_fitting.py: Contains code for fitting shape models to contours
     thresholding.py: Threshold out green or white backgrounds, and trace the contour
     Vector2D.py: Basic 2D geometry package with some easy tools for what we're doing
     pickle_utils.py: Used for saving our models to disc
     TopicUtils.py: Provides methods for pulling a single message from a topic, as we need to do image processing when the camera is in streaming mode

     ----------------
     clothing_models
     ----------------
     Brief: Provides the models we use for shape fitting, and an example of how to run them on image files

     Scripts/Nodes:

     birdseye_maker.py: Converts images to the birdseye view (should be in utils)
     comparer.py: Used for comparing predicted and gruond-truth points
     model_maker.py: Used to design new clothing shape models
     fold_maker.py: Used to generate new models from existing ones by adding a fold line
     fit_cloth_models.py: Example of how to run the fitting procedure

~~~~~~~~~~~~~~
berkeley_utils
~~~~~~~~~~~~~~

Brief:
Misc packages we've developed that didn't necessarily fit under another stack.

    -----------
    stereo_click
    ------------
    Brief: Lets us click on points in an updating camera feed, and streams 3D points out.

    Scripts/Nodes
    click_window.py: The GUI we use for clicking
    mono_converter.py: Determines 3D from a single image assuming intersection with a known plane in the world frame (i.e. a table)
    stereo_converter.py: Determines 3D from two images where corresponding points are clicked.
    optical_converter.py: Determines 3D from time-delayed images (taken from different views)

    Launch Files:
    wide_[mono/stereo/optical]_click.launch: An example of how to use these tools


    --------------
    shape_window
    -------------
    Brief: A GUI interface for dealing with shapes, buttons, etc. Used by folding_geometry.FoldingGUI

    Src/Include Files:
    Geometry2D.py: Package for 2D shapes/geometry. Used extensively by the folding_geometry package
    ShapeWindow.py: Introduces the ShapeWindow class.

    -----------
    rll_utils
    -----------
    Brief: Utility functions for dealing with certain, frequently repeated bits of code

    Src/Include Files:
    RosUtils: Provides functions for dealing with Services and Topics
    TFUtils: Functions which make it easier to convert between frames assuming we always want now() + error handling
    ImageUtils: Wrappers for img_msg -> cv_image conversions

    -----------
    smach_utils
    -----------
    Brief: Some extensions to Smach state machines, mostly allowing for easy nesting


~~~~~~~~~~~~~~
berkeley_demos
~~~~~~~~~~~~~~~

Brief: Where we throw end-to-end demos we are working on. At this point, it's easiest to ignore all but one:

    ----------------
    unfolding_smach
    ----------------
    Brief: The robot takes a towel from an arbitrary configuration, and folds it
    
    Demo Setup: Assumes we have a table with a green cloth on it, and some sort of padding which lets the robot get
    under the clothing easily. The table must be narrow enough for the robot's arms to be able to reach across from one
    end to the other (left <-> right). 

    A towel begins on the table in an arbitrary pile. The robot will pick it up, drag it across the table, grab the lowest
    hanging point, and repeat a few times. It will then shake it, and have it in a two-triangle configuration, where one
    triangle is face up and the other is face down. It shakes it, lays it out, and observes it. It grabs what it observed
    to be the top half, spreads it out as a rectangle. It then picks it up and flips it, to smooth corners it couldn't fix
    before. Then it folds it on half, followed by thirds.

    Scripts/Nodes:
    For our purposes, only one: demo.py. This simply needs to be run.

    Launch files:
    There are a few, all of which are combined in full_demo.launch. Ideally, you can launch full_demo.launch, run demo.py, and need nothing else.
    
Something went wrong with that request. Please try again.