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Behavior Domain Definition Language
The Behavior Domain Definition Language (BDDL) is a domain-specific language designed for the Benchmark for Everyday Household Activities in Virtual, Interactive, and ecOlogical enviRonments (BEHAVIOR).
BDDL is a predicate logic-based language inspired by, but distinct from, the Planning Domain Definition Language . It defines each BEHAVIOR activity definition as a BDDL
problem, consisting of of a categorized object list (
:objects), an initial condition that has only ground literals (
:init), and a goal condition that is a logical expression (
To install this implementation of BDDL, clone this repository locally:
git clone https://github.com/StanfordVL/bddl.git
then run setup:
cd bddl python setup.py install
Example BDDL activity
(define (problem cleaning_the_pool_0) (:domain igibson) (:objects pool.n.01_1 - pool.n.01 floor.n.01_1 - floor.n.01 scrub_brush.n.01_1 - scrub_brush.n.01 shelf.n.01_1 - shelf.n.01 detergent.n.02_1 - detergent.n.02 sink.n.01_1 - sink.n.01 agent.n.01_1 - agent.n.01 ) (:init (onfloor pool.n.01_1 floor.n.01_1) (stained pool.n.01_1) (onfloor scrub_brush.n.01_1 floor.n.01_1) (onfloor detergent.n.02_1 floor.n.01_1) (inroom shelf.n.01_1 garage) (inroom floor.n.01_1 garage) (inroom sink.n.01_1 storage_room) (onfloor agent.n.01_1 floor.n.01_1) ) (:goal (and (onfloor ?pool.n.01_1 ?floor.n.01_1) (not (stained ?pool.n.01_1) ) (ontop ?scrub_brush.n.01_1 ?shelf.n.01_1) (onfloor ?detergent.n.02_1 ?floor.n.01_1) ) ) )
:init sections specify the initial state than an agent will start in, located as specified in
inroom predicate specifies which scene objects must be present, and other binary kinematic predicates (
inside, etc.) specify where small objects should be sampled. The BDDL functionality sends a representation of these conditions to sampling functionality implemented in a simulator (such as iGibson 2.0) to be sampled into a physical instance of the activity.
:goal section specifies the condition that the agent must satisfy to be successful on the activity. BDDL is entirely process-agnostic, specifiying only the simulator state that must be reached for success.
Example code usage
You will typically want to use BEHAVIOR activities with a simulator. To use a BEHAVIOR activity without a simulator, use the following code.
from bddl.activity_base import BEHAVIORActivityInstance behavior_activity = "storing_the_groceries" # the activity you want to try, full list in bddl/bddl/activity_conditions activity_definition = 0 # the specific definition you want to use. As of BEHAVIOR100 2021, this should always be 0. behavior_activity_instance = BEHAVIORActivityInstance(behavior_activity=behavior_activity, activity_definition=activity_definition)
To use a BEHAVIOR activity with a simulator, create a subclass of
BEHAVIORActivityInstance for your simulator. Example for iGibson 2.0. This will require an implementation of sampling functionality or pre-sampled scenes that satisfy the activity's initial condition and implementation for checking each type of binary kinematic predicate (e.g.
nextto) and unary nonkinematic predicate (e.g.
Logic evaluator for goal
When using BEHAVIOR activities with a simulator, the goal condition is evaluated at every simulator step by calling
simulator_activity_instance is some subclass of
bddl.condition_evaluation contain this functionality. Atomic formulae that interface directly with the simulator are implemented in
bddl.logic_base. These require the simulator checking functions for various predicates to be implemented, and are the leaf nodes of the compositional expression making up a goal condition or the list of literals making up an initial condition. Logical operators are implemented in
bddl.condition_evaluation, and form a compositional structure of the condition to evaluate.
Solver for ground goal solutions
bddl.condition_evaluation also contains basic functionality to generate ground solutions to a compositional goal condition, including one that may contain quantification. This functionality is much like a very simple, unoptimized logic program, and will return a subset of solutions in cases where the solution set is too large to compute due to exponential growth.
To test the predicate evaluator, run
pytest in project root.
To add a test, create a new python file under the tests directory, and add
additional functions prefixed with
test_ which include assert statements that
should evaluate true.
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