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Thimblerigger Challenge of the NRP

This repository defines the challenge perception "Thimblerigger". It is part of the KIT course "Virtual Neurorobotics".

This repo is for the challenge definition only, to create a solution please fork this repository and implement your solution there.

Installing

Assuming that you have a local install of the NRP, no special setup is needed. Simply fork this repo into the Experiments folder. When starting the NRP frontend, you should now see an experiment called PerceptionChallengeKIT.

Running the experiment

Start the experiment. You will be in a paused state with nothing in the world but an iCub Robot. Once you hit the play button, three red cylinders will spawn in front of the robot. These cylinders represent 3 mugs. One of the mugs contains a green ball. The goal of the challenge is to:

  • Check which mug contains the ball
  • Track that mug during shuffling. Shuffling computes n random permutations of the mug order and moves the mugs into the corresponding positions. At most 2 mugs are displaced at one time, one to either side of the original lane.
  • Tell which mug contains the ball in any way, e.g.: Publish an index, point the robot to the correct mug, ...

Interacting with the experiment

The user can manually navigate through the challenge by sending the following ROS service requests. You can do this via command line, or rospy.ServiceProxy objects. (The challenge does nothing if you do not send these requests, it is an "interactive" challenge):

Service name Service Type Description
/thimblerigger/reset std_srvs/Trigger Despawns the mugs, respawns them in the starting sorted order and chooses a new mug under which the ball is contained
/thimblerigger/show_correct_mug std_srvs/Trigger Lifts up the mug under which the ball is contained and shows the ball
/thimblerigger/hide_correct_mug std_srvs/Trigger Hides the ball again under the mug if it was lifted up
/thimblerigger/shuffle std_srvs/Trigger Randomly permutes the mugs n times. n is a parameter of the thimblerigger challenge.

You can also use a stepping mechanism (implemented in the state machine):

Service name Service Type Description
/thimblerigger/start_challenge std_srvs/Trigger Once the simulation is running, starts the thimblerigger state machine
/thimblerigger/step_challenge std_srvs/Trigger Steps through the challenge in this order (one step per service call): Show correct mug - Hide correct mug - Shuffle - Show correct mug - (Then the state machine exits, feel free to implement a loop/reset mechanism)
/thimblerigger/stop_challenge std_srvs/Trigger Currently not used

Training signal

There is a training signal for supervised training being published under the topic /thimblerigger/training_signal. It publishes the index of the correct mug, as soon as it is known (meaning before the /thimblerigger/show_correct_mug service is called, a -1 is published).

The 3 indices [0, 1, 2] are arranged as follows: 0 is closest to the default robot starting position, 2 the furthest away. If you reset the challenge with the service call, the training signal will be reset, too. The index switches every time the mug has reached its goal position during shuffling, but not in between.

Auto-stepper

If you don't want/need to manually trigger the different stages of the challenge via the ROS service calls, there is an auto-stepper which can automatically trigger the stages of the challenge for you. It is implemented in stepper.py. To use it, simply uncomment the last two lines in the state machine.

What am I allowed to modify?

What you shouldn't modify

There is a configuration file thimblerigger_config.py, that allows to configure things like topic names. The paragraph above assumes that you have not changed those.

You should not fiddle with the following files (and any references to one of these files in any configuration file):

  • state_machine.exd
  • stepper.py
  • thimblerigger_server.py
  • thimblerigger.py (this is where most interesting stuff happens)

Feel free to read through them for further information how the challenge works internally, the thimblerigger*.py files are well documented. You can find doxygen pages in the docs folder.

Things that you should definitely change

In bibi_configuration.bibi:

  • There is a file-tag within brainModel that points to a file that load the brain of the robot. This is relative to the Models folder in the NRP. On a local install, it is best to create a new file within the experiment repo, and put a path relative to the Models directory in this tag. (This is to avoid splitting the solution into multiple repos). For example:
<brainModel>
    <file>../Experiments/hbpprak_perception/my_custom_brain.py</file>
</brainModel>

will allow you to have the brain file within your solution fork of the challenge repo.

  • Add transfer functions, spike recorders, etc. in the bibi file, see the documentation.

Things that you might want to change

In thimblerigger_config.py:

  • num_mugs, num_shuffles and seed. The final challenge uses 3 cups, 1 shuffle, and the seed for shuffling and choice of which mug contains the ball should be random (i.e. seed = None).
  • movement_rate: This parameter can be used to slow down movement of the mugs. The default value of None does not slow it down at all. This can be useful if your brain simulation is too slow and the camera does not pick up intermediate frames. Lower values correspond with slower movement.

Additionally, you might want to modify:

In ExDPerceptionChallengeKIT.exc:

  • The timeout in seconds (for debugging). Default is 10 minutes.
  • The robot starting position (robotPose-tag)

Additional Information