ikflow 0.0.8

Open source implementation of the 'IKFlow' inverse kinematics solver

IKFlow

Normalizing flows for Inverse Kinematics. Open source implementation to the paper "IKFlow: Generating Diverse Inverse Kinematics Solutions"

Setup, inference only

pip install git+https://github.com/jstmn/ikflow
# or
git clone https://github.com/jstmn/ikflow.git && pip install ikflow/

Setup - training, inference, and visualization

The following section outlines the setup procedures required to run the visualizer that this project uses. The only supported OS is Ubuntu. Visualization may work on Mac and Windows, I haven't tried it though. For Ubuntu, there are different system wide dependencies for Ubuntu > 21 and Ubuntu < 21. For example, qt5-default is not in the apt repository for Ubuntu 21.0+ so can't be installed. See https://askubuntu.com/questions/1335184/qt5-default-not-in-ubuntu-21-04.

Ubuntu >= 21.04

sudo apt-get install -y qtbase5-dev qtchooser qt5-qmake qtbase5-dev-tools libosmesa6 build-essential qtcreator
export PYOPENGL_PLATFORM=osmesa # this needs to be run every time you run a visualization script in a new terminal - annoying, I know

Ubuntu <= 20.x.y

(This includes 20.04 LTS, 18.04 LTS, ...)

sudo apt-get install -y qt5-default build-essential qtcreator

Lastly, install with pip:

git clone https://github.com/jstmn/ikflow.git && cd ikflow
pip install -e ".[dev]"

Getting started

> Example 1: Use IKFlow to generate IK solutions for the Franka Panda arm

Evaluate the pretrained IKFlow model for the Franka Panda arm. Note that this was the same model whose performance was presented in the IKFlow paper. Note that the value for model_name - in this case panda_tpm should match an entry in model_descriptions.yaml

python scripts/evaluate.py --testset_size=500 --model_name=panda_full_tpm

> Example 2: Visualize the solutions returned by the panda_tpm model

Run the following:

python scripts/visualize.py --model_name=panda_full_tpm --demo_name=oscillate_target

Run an interactive notebook: jupyter notebook notebooks/robot_visualizations.ipynb

> Example 3: Run IKFlow yourself

Example code for how to run IKFlow is provided in examples/example.py. A sample excerpt:

ik_solver, _ = get_ik_solver(args.model_name)
target_pose = np.array([0.5, 0.5, 0.5, 1, 0, 0, 0])
number_of_solutions = 5
solutions, l2_errors, angular_errors, joint_limits_exceeded, self_colliding, runtime = ik_solver.solve(
  target_pose, number_of_solutions, refine_solutions=False, return_detailed=True
)

Notes

This project uses the w,x,y,z format for quaternions. That is all.

Training new models

The training code uses Pytorch Lightning to setup and perform the training and Weights and Biases ('wandb') to track training runs and experiments. WandB isn't required for training but it's what this project is designed around. Changing the code to use Tensorboard should be straightforward (so feel free to put in a pull request for this if you want it :)).

First, create a dataset for the robot:

python scripts/build_dataset.py --robot_name=panda --training_set_size=25000000 --only_non_self_colliding

Then start a training run:

# Login to wandb account - Only needs to be run once
wandb login

# Set wandb project name and entity
export WANDB_PROJECT=ikflow 
export WANDB_ENTITY=<your wandb entity name>

python scripts/train.py \
    --robot_name=panda \
    --nb_nodes=12 \
    --batch_size=128 \
    --learning_rate=0.0005

Common errors

1. GLUT font retrieval function when running a visualizer. Run export PYOPENGL_PLATFORM=osmesa and then try again. See https://bytemeta.vip/repo/MPI-IS/mesh/issues/66

Traceback (most recent call last):
  File "visualize.py", line 4, in <module>
    from ikflow.visualizations import _3dDemo
  File "/home/jstm/Projects/ikflow/utils/visualizations.py", line 10, in <module>
    from klampt import vis
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/klampt/vis/__init__.py", line 3, in <module>
    from .glprogram import *
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/klampt/vis/glprogram.py", line 11, in <module>
    from .glviewport import GLViewport
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/klampt/vis/glviewport.py", line 8, in <module>
    from . import gldraw
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/klampt/vis/gldraw.py", line 10, in <module>
    from OpenGL import GLUT
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/OpenGL/GLUT/__init__.py", line 5, in <module>
    from OpenGL.GLUT.fonts import *
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/OpenGL/GLUT/fonts.py", line 20, in <module>
    p = platform.getGLUTFontPointer( name )
  File "/home/jstm/Projects/ikflow/venv/lib/python3.8/site-packages/OpenGL/platform/baseplatform.py", line 350, in getGLUTFontPointer
    raise NotImplementedError( 
NotImplementedError: Platform does not define a GLUT font retrieval function

2. If you get this error: tkinter.TclError: no display name and no $DISPLAY environment variable, add the lines below to the top of ik_solvers.py (anywhere before import matplotlib.pyplot as plt should work).

import matplotlib
matplotlib.use("Agg")

TODO

1. Add CPU versions of pretrained model
2. Add 'light' pretrained models. These are smaller networks for faster inference
3. Include a batched jacobian optimizer to enable parallelized solution refinement. Note: This is in the works in a seperate repository

Citation

@ARTICLE{9793576,
  author={Ames, Barrett and Morgan, Jeremy and Konidaris, George},
  journal={IEEE Robotics and Automation Letters}, 
  title={IKFlow: Generating Diverse Inverse Kinematics Solutions}, 
  year={2022},
  volume={7},
  number={3},
  pages={7177-7184},
  doi={10.1109/LRA.2022.3181374}
}