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LTAMP
Learning for Task and Motion Planning (LTAMP)
Overview
Robotic multi-step manipulation planning for a PR2 robot using both learned and engineered models of primitive actions.
- Learned: pour, push, scoop, stir
- Engineered: move, pick, place, press
References
Zi Wang*, Caelan Reed Garrett*, Leslie Pack Kaelbling, Tomás Lozano-Pérez. Learning compositional models of robot skills for task and motion planning, The International Journal of Robotics Research (IJRR), 2020.
Zi Wang, Caelan R. Garrett, Leslie P. Kaelbling, Tomás Lozano-Pérez. Active model learning and diverse action sampling for task and motion planning, International Conference on Intelligent Robots and Systems (IROS), 2018.
Installation
$ git clone --recursive git@github.com:caelan/LTAMP.git
$ cd LTAMP
LTAMP$ ./setup.bash
Inverse Kinematics (IK)
setup.py - compiles an IKFast analytical IK program for both of the PR2's arms
LTAMP$ cd control_tools/ik/
LTAMP$ control_tools/ik/$ python setup.py build
See README for details about using the existing and generating new IK solvers.
Examples
Planning, experimentation, and learning in a simulated PyBullet environment.
Planning
run_simulation.py: tests the planning module in simulation
LTAMP$ python -m plan_tools.run_simulation -h
usage: run_simulation.py [-h] [-c] [-e] [-p {test_block,test_coffee,test_holding,test_pour,test_push,test_shelves,test_stack_pour,test_stacking,test_stir}] [-s SEED] [-v] [-d]
optional arguments:
-h, --help show this help message and exit
-c, --cfree When enabled, disables collision checking (for debugging).
-e, --execute When enabled, executes the plan using physics simulation.
-p {test_block,test_coffee,test_holding,test_pour,test_push,test_shelves,test_stack_pour,test_stacking,test_stir}, --problem {test_block,test_coffee,test_holding,test_pour,test_push,test_shelves,test_stack_pour,test_stacking,test_stir}
The name of the problem to solve.
-s SEED, --seed SEED The random seed to use.
-v, --visualize_planning
When enabled, visualizes planning rather than the world (for debugging).
-d, --disable_drawing
When enabled, disables drawing names and forward reachability.
Data Collection
collect_simulation.py: collects manipulation-primitive data in simulation
LTAMP$ python -m learn_tools.collect_simulation -h
usage: collect_simulation.py [-h] [-f FN] [-n NUM] -p {pour,push,scoop,stir} [-t TIME] [-v]
optional arguments:
-h, --help show this help message and exit
-f FN, --fn FN The parameter function to use.
-n NUM, --num NUM The number of samples to collect.
-p {pour,push,scoop,stir}, --problem {pour,push,scoop,stir}
The name of the skill to learn.
-t TIME, --time TIME The max planning runtime for each trial.
-v, --visualize When enabled, visualizes execution.
Modules
Planning
The planning module generates plans using the learned primitives.
Relevant planning submodules:
- pddlstream - Task and Motion Planning (TAMP)
- ss-pybullet - PyBullet Robot Planning
Learning
The learning module conducts manipulation-primitive data collection experiments and learns models from the collected data.
Control
The control module provides an interface for executing both simulated and real motion.