1. openEASE: this is where the result of your coding efforts will be deployed.
  2. Paper: an overview paper about openEASE.
  3. Here: prolog/knowrob_vis.pl holds the predicates currently available for displaying information on openEASE.
  4. Here: the MongoDB interface for KnowRob (the underlying knowledge base “for robots”). Here, sub-symbolic (sensor) information from the log files is saved. prolog/knowrob_mongo.pl lets you look at the predicates currently available for getting sub-symbolic information from a log.
  5. Here: access to the symbolic information extracted from logs; prolog/knowrob_plan_logs.pl holds the main predicates.

Running openEASE locally

Here: pick and place experiments. It includes:

  • an OWL file cram_log.owl, reflecting the semantic structure of the experiment,
  • a couple of .json files holding sub-symbolic information (TF, tf.json) and description entities (Designators, logged_designators.json); import them to MongoDB:
mongoimport --db roslog --collection tf < tf.json
mongoimport --db roslog --collection logged_designators < logged_designators.json
  • pictures taken by the robot during some actions, referenced in the OWL file.

Now install the OpenEASE and KnowRob locally via Docker. This should already contain a sample pick and place experiment; for importing new ones, you will need to rebuild the Docker containers here.

For forked GitHub repository the dev files are here on the indigo-devel branch. knowrob/docker and knowrob_webtools, both master branch, are also needed. All the rest is as described here.