Jrl

Jrl ('Jeremy's robotics library') is a robotics library containing robot models for popular robots as well as efficient, pytorch based parallelized implementations of forward kinematics, inverse kinematics, end effector jacobian, and robot-robot + robot-environment collision checking. This library is used as the backbone for the IKFlow and CppFlow codebases.

Robots

Robot models include the following:

| Robot | Class name | Alias | |-----------------------------| --------- | --------- | | Franka Panda | Panda | panda | | Fetch | Fetch | fetch | | Fetch - Arm (no lift joint) | FetchArm | fetch_arm | | Kuka LBR IIWA7 | Iiwa7 | iiwa7 | | Flexiv Rizon 4 | Rizon4 | rizon4 | | Ur5 | Ur5 | ur5 |

Robots can easily be visualized with uv run python scripts/visualize_robot.py --robot_name=<alias>, where <alias> is one from the table above.

<table> <tr> <td><pre>panda</pre><img src="media/panda.png"></td> <td><pre>iiwa7</pre><img src="media/iiwa7.png"></td> <td><pre>iiwa14</pre><img src="media/iiwa14.png"></td> </tr> <tr> <td><pre>fetch</pre><img src="media/fetch.png"></td> <td><pre>rizon4</pre><img src="media/rizon.png"></td> <td><pre>ur5</pre><img src="media/ur5.png"></td> </tr> </table>

Functionality

Available operations include (all part of the Robot class):

| function | description | |--------------------------------------------------------|-----------------------------------------------------------------------------------------------| | forward_kinematics() | (batched) forward kinematics | | jacobian() | (batched) Jacobian of the manipulators forward kinematics map (w.r.t. joint angles) | | inverse_kinematics_step_levenburg_marquardt() | (batched) Inverse kinematics step using Levenburg-Marquardt | | inverse_kinematics_step_jacobian_pinv() | (batched) Inverse kinematics step using the jacobian pseudo-inverse method | | self_collision_distances() | (batched) Pairwise distance between each link of the robot | | self_collision_distances_jacobian() | (batched) Jacobian of self_collision_distances() w.r.t. joint angles | | env_collision_distances() | (batched) Pairwise distance between each link of the robot and each cuboid in the environment | | env_collision_distances_jacobian() | (batched) Jacobian of env_collision_distances() w.r.t. joint angles |

Quickstart code

This code will load a Panda robot model and then run forward and inverse kinematics on randomly sampled configs. See demo.py for the complete script, which includes robot-robot and robot-environment collision checking.

from jrl.robots import Panda
from jrl.evaluation import pose_errors_cm_deg
import torch

def assert_poses_almost_equal(poses_1, poses_2):
    pos_errors_cm, rot_errors_deg = pose_errors_cm_deg(poses_1, poses_2)
    assert (pos_errors_cm.max().item() < 0.01) and (rot_errors_deg.max().item() < 0.1)

robot = Panda()
joint_angles, poses = robot.sample_joint_angles_and_poses(n=5, return_torch=True) # sample 5 random joint angles and matching poses

# Run forward-kinematics
poses_fk = robot.forward_kinematics(joint_angles) 
assert_poses_almost_equal(poses, poses_fk)

# Run inverse-kinematics
ik_sols = joint_angles + 0.1 * torch.randn_like(joint_angles) 
for i in range(5):
    ik_sols = robot.inverse_kinematics_step_levenburg_marquardt(poses, ik_sols)
assert_poses_almost_equal(poses, robot.forward_kinematics(ik_sols))

Note: This project uses the w,x,y,z format for quaternions.

Installation

Clone the repo and install with uv. Don't use the version on pypi - it will remain out of date until this project hardens.

git clone https://github.com/jstmn/Jrl.git && cd Jrl/
uv sync
uv pip install -e .

Acknowledgements

• klampt is used as a visualizer, for testing the Jrl FK / jacobian calculations, and for single configuration collision checking
• David Millard developed the collision checking functionality, improved the efficiency of the FK code, and wrote the jacobian function.
• kinpy as a second FK library for comparing against the internal batch_fk implementation, and the klampt FK version
• YupuLu for contributing a number of the robot urdfs