Affordance imitation

Modules

This is the general architecture currently under development

(updated 28/07/09)

BlobSegmentation

Implemented by edisonSegmentation module in the iCub repository. Module that takes a raw RGB image as input and provides a segmented (labeled) image at the output, indicating possible objects or object parts present in the scene.

Ports:

• /conf
• /rawimg:i
• /rawimg:o
• /labelimg:o
• /viewimg:o

BlobDescriptor

Implemented by blobDescriptor in the iCub repository. Module that receives a labeled image and the corresponding raw image and creates descriptors for each one of the identified objects.

Ports (not counting the prefix /blobDescriptor):

• /rawImg:i
• /labeledImg:i
• /rawImg:o
• /viewImg:o - image with overlay edges
• /affDescriptor:o
• /trackerInit:o - colour histogram and parameters that will serve to initialize a tracker, e.g., CAMSHIFT

EffectDetector

Implemented by effectDetector module in the iCub repository.
Algorithm:

  1.wait for initialization signal and parameters on /init
  2.read the raw image that was used for the segmentation on /rawsegmimg:i
  3.read the current image on /rawcurrimg:i
  4.check if the the ROI specified as an initialization parameter is similar in the two images
  5.if (similarity<threshold) 
  6.  answer 0 on /init and go back to 1.
  7.else
  8.  answer 1 on /init
  9.  while(not received another signal on /init)
 10.    estimate the position of the tracked object
 11.    write the estimate on /effect:o
 12.    read a new image on /rawcurrimg:i
 13.  end
 14.end

Ports:

• /init //receives a bottle with (u, v, width, height, h1, h2, ..., h16, vmin, vmax, smin), answers 1 for success or 0 for failure.
• /rawsegmimg:i //raw image that was used for the segmentation
• /rawcurrimg:i
• /effect:o //flow of (u,v) positions of the tracked object
• /error:o //signals if the module is working correctly (0) or if there is some problem (1..)

QueryCollector

Implemented by demoAff module in the iCub repository. Module that receives inputs from the object descriptor module and the effect descriptor. When activated by the behavior controller, info about the objects is used to select actions that will be executed by the robot. In the absence of interaction activity, it notifies it to the behavior controller that may decide to switch to another behavior.

Ports:

• /demoAffv2/effect
• /demoAffv2/synccamshift
• /demoAffv2/objsdesc
• /demoAffv2/
• /demoAffv2/motioncmd
• /demoAffv2/gazecmd
• /demoAffv2/behavior:i
• /demoAffv2/behavior:o
• /demoAffv2/out
• /demoAffv2/out

Ports and communication

The interface between modules is under development. The current version (subject to changes as we refine it) is as follows:

• Behavior to AttentionSelection -> vocabs "on" / "off"
• Behavior to Query -> vocabs "on" / "off". We should add some kind of context to the on command (imitation or learning being the very basic).
• Gaze Control -> Behavior: read the current head state/position
• Query to Behavior -> "end" / "q"
• Query to Effect Detector. The main objective of this port is to start the tracker at the object of interest. We need to send at least:
  • position (x,y) within the image. 2 doubles.
  • size (h,w). 2 doubles.
  • color histogram. TBD.
  • saturation parameters (max min). 2 int.
  • intensity (max min). 2 int.

• Effect Detector to Query
• Camshiftplus format

• blobDescriptor -> query
  • Affordance descriptor. Same format as camshiftplus
  • tracker init data. histogram (could be different from affordance) + saturation + intensity

• query -> object segmentation
  • vocab message: "do seg"

• object segmentation -> blob descriptor
  • labelled image
  • raw image