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Real Robot Experiments with CoSTAR

This code requires the latest version of the open source CoSTAR stack.

Starting Real Robot

Just run:

roslaunch ctp_integration bringup.launch

This is configured specifically to work with the JHU UR5, and may not work (or may require some adaptation) if you want to use it with a different robot.

Optionally, you can start the instructor UI to make it easier to set up tasks. This command is:

roslaunch instructor_core instructor.launch

The CoSTAR UI is designed to make data collection and specifying simple tasks easier. If you want to put the robot in freedrive mode, press "TEACH" at the top of the UI.

CoSTAR Instructor UI

You can use the UI to construct simple task plans and save them as Behavior Trees, and to control robot functionality.

Run Data Collection

The command for data collection is:

rosrun ctp_integration run.py

For example, to start collection for 100 samples:

# Use the --execute flag to loop for a certain number of trials
rosrun ctp_integration run.py --execute 100

To restart partway through:

# Start at example number 38
rosrun ctp_integration run.py --execute 100 --start 38

About the Data Collection Tool

Data collection will call the update() function after every action, which saves the current joint states, servos the arm out of the way, and updates scene information before returning the arm to its original position.

The data collection tool by default saves .h5f files to the $HOME/.costar/data directory, though you may want to change this. It will save a number of different fields:

# Field and shape of output for a trial lasting 141 frames = 14.1 seconds
('info', (141,))
('object', (141,))
('image', (141,))
('pose', (141, 7))
('label', (141,))
('q', (141, 6))
('camera', (141, 7))
('labels_to_name', (40,))
('object_pose', (141, 7))
('dq', (141, 6))

These are:

  • info: string description of which stage of SmartMove (high-level action) the robot is in
  • object: unique object ID, assigned via perception system
  • image: compressed image (JPEG encoding)
  • pose: x,y,z + quaternion, end effector pose relative to robot base
  • label: integer index into action labels list
  • q: current joint positions
  • camera: x,y,z, + quaternion, camera pose relative to robot base
  • labels_to_name: list of strings for action name
  • object_pose: x,y,z + quaternion, pose of object being manipulated if possible
  • dq: current joint velocities

As of March 14, 2018, the list of actions in labels_to_name returns:

['place_green_on_yellow', 'place_yellow_on_greenblue',
 'place_blue_on_yellowred', 'place_yellow_on_red', 'place_blue_on_red',
 'grab_blue', 'place_red_on_blueyellow', 'place_green_on_redyellow',
 'place_red_on_yellow', 'place_green_on_blueyellow', 'place_red_on_greenblue',
 'place_blue_on_green', 'place_blue_on_redgreen', 'place_yellow_on_blue',
 'place_blue_on_greenyellow', 'place_blue_on_yellowgreen',
 'place_blue_on_greenred', 'place_yellow_on_redgreen', 'grab_yellow',
 'place_red_on_greenyellow', 'grab_green', 'place_red_on_green',
 'place_yellow_on_bluered', 'place_yellow_on_green', 'place_green_on_blue',
 'place_yellow_on_bluegreen', 'place_blue_on_redyellow', 'place_red_on_blue',
 'place_red_on_yellowgreen', 'place_yellow_on_greenred',
 'place_green_on_yellowblue', 'place_red_on_bluegreen', 'place_green_on_red',
 'place_red_on_yellowblue', 'place_green_on_yellowred',
 'place_green_on_redblue', 'grab_red', 'place_yellow_on_redblue',
 'place_green_on_bluered', 'place_blue_on_yellow']

There are a total of 40 actions (also referred to as options throughout the code, to differentiate them from low-level actions). We also add in an extra symbol for the "null" option, corresponding to no action having been selected.

Data Preprocessing

Use the preproccessing script in costar_models:

# With ROS
rosrun costar_models ./costar_models/scripts/preprocess_images \
    --data_file robot.h5f --cpu
# Without ROS
$COSTAR_PLAN/costar_models/scripts/preprocess_images \
    --data_file robot.h5f --cpu

Data will be placed in the directory name prepended with "small". In the above, we see the directories "robot" and "small_robot", each filled with ".h5f" files.

A note on the --cpu flag: this is just to tell tensorflow it does not need the GPU for this script, really it should not need this anyway because we do not use tensorflow here.

Training Models

Pretrain Image Autoencoder

Conditional Image Model

The conditional image model is supposed to train the whole pipeline now.

# With ROS
rosrun costar_models ctp_model_tool --model conditional_image \
    --dropout_rate 0.1 --data_file small_robot2.h5f --lr 0.001 \
    --features costar -e 150 --model_directory $HOME/.costar/models \
    --skip_connections 1 --use_ssm 1 --batch_size 64 --no_disc \
    --steps_per_epoch 300 --retrain
# Without ROS
$COSTAR_PLAN/costar_models/scripts/ctp_model_tool --model conditional_image \
    --dropout_rate 0.1 --data_file small_robot2.h5f --lr 0.001 \
    --features costar -e 150 --model_directory $HOME/.costar/models \
    --skip_connections 1 --use_ssm 1 --batch_size 64 --no_disc \
    --steps_per_epoch 300 --retrain

Rundown of flags:

  • --retrain tells it to re-learn the encoder and decoder
  • --data_file tells it which directory and file format to load data from
  • --lr sets the learning rate
  • --no_disc tells it to use a simplified model without the augmented loss
  • --features costar tells it to load the costar (real robot) version of the training model setup
  • --model_directory will tell it where models and results are to be saved
  • --dropout_rate 0.1 sets 10% dropout
  • --skip_connections 1 activates skip connections in the transform
  • --use_ssm 1 enables a spatial softmax in the transform
  • --batch_size 64 tells it to use batches of 64 examples
  • --steps_per_epoch tells it how often to update metrics and compute validation performance

Debugging

Debugging CoSTAR Arm

The CostarArm class manages activity like the SmartMoves we use to manipulate objects.

You can just kill the UR5 server script and restart it with:

rosnode kill /simple_ur5_driver_node
rosrun costar_robot_manager ur_driver.py

This can be useful if you want to debug any particular aspect of the Arm, such as the SmartMoves.