Package: guided-tamp-benchmark
GuidedTAMPBenchmarkThis repository contains code for benchmarking/evaluating task-and-motion-planners guided by video demonstration.
The code was used in ICRA 2023 submission entitledMulti-Contact Task and Motion Planning Guided by Video Demonstration:@inproceeding{2023ICRA_Zorina,author={Zorina, Kateryna and Kovar, David and Lamiraux, Florent and Mansard, Nicolas and Carpentier, Justin and Sivic, Josef and Petrik, Vladimir},title={{Multi-contact task and motion planning guided by video demonstration.}},booktitle={ICRA 2023-International Conference on Robotics and Automation},year={2023}}Additional resources:ICRA2023 planner source code,Project page,Video overviewInstallationgitclonehttps://github.com/agimus-project/guided_tamp_benchmark.git
condacreate-ngtamppython=3.10poetry
condaactivategtamp
poetryinstallUsageTo evaluate your planner, you need to implement all abstract methods fromcore.planner.BasePlannerclass.
Then you can use our benchmarking script to evaluate it, for example:task=ShelfTask(1,PandaRobot(),2)b=Benchmark()b.do_benchmark(task=task,planner=YourPlannerCls,seeds=[1],planner_arg={"verbose":True,},max_planning_time=60,delta=0.001,)See ourexamplesdirectory for more information or our ICRA2023 plannerhttps://github.com/agimus-project/tamp_guided_by_videothat utilizes this benchmark for the evaluation.Benchmark variabilityTasksThere are three tasks implemented: Shelf Task, Waiter Task, and Tunnel Task.
Here is an example of task initialization:fromguided_tamp_benchmark.tasksimportShelfTaskfromguided_tamp_benchmark.models.robotsimport*task=ShelfTask(0,PandaRobot(),1)In this example, a Shelf Task with the first demonstration (i.e., number 0) is created with Panda Robot which base is placed at the reference pose number 1.Shelf TaskThe shelf task is composed of a table, a shelf and a varying
set of objects that the robotic manipulator is supposed to
arrange, i.e. to move the objects to the predefined poses
on the table or on the shelf. The complexity of the task
is controlled by the number of objects that the robot should
arrange. This task is challenging for state-of-the-art planners
because it requires moving multiple objects in a single
planning task.Tunnel TaskThe tunnel task consists of a tunnel and a single object that
should be transferred through the tunnel. The tunnel is thin
enough so that the robot can place an object inside the tunnel
on one side and pick it up from the other side. The challenge
lies in the narrow passage in the admissible configuration
space that needs to be discovered by the planner.Waiter TaskThe waiter task simulates the job of waiter, in which a set of
objects needs to be transferred from one location to another
distant location. Waiters use a tray to minimize the walked
distance. In our simulation, a mobile robot is equipped with
a tray-like space that it can use for transferring objects.
Discovering the tray in the planning state-space is non-trivial,
which makes this task challenging for the planners that do
not utilize demonstrationsRobotsWe implemented following robots for benchmarking:Panda robot (fixed base, 7DoF),UR5 robot (fixed base, 6DoF),KUKA IIWA robot (fixed base, 7DoF),KUKA KMR IIWA robot (mobile base, 7+3DoF).Code structureCoreInguided_tamp_benchmark.coreis description of base planner function that is compatible with benchmarking script and is to be used as template.
Description of configuration class, used for storing scene configuration, and path class, which is a template class for storing path planned by planner is also there.ModelsInguided_tamp_benchmark.modelsis description of robots, furniture and objects used in different tasks of benchmark.ScriptsInguided_tamp_benchmark.scriptsis class benchmark that can be used for running benchmark on planner.TasksInguided_tamp_benchmark.tasksare classes with descriptions of benchmarking tasks, renderer, demonstration class used for storing and loading demonstration files saved as .pkl files and collision class used for collision checking for path evaluation.AcknowledgmentThis work is part of theAGIMUSproject, funded by the European Union under GA no.101070165. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. This work was funded by the European Regional Development Fund under the projectIMPACT(reg. No. CZ.02.1.01/0.0/0.0/15_003/0000468), the Grant Agency of the Czech Technical University in Prague, grant No. SGS21/178/OHK3/3T/17.