jrl 0.0.9

Jeremy's Robotics Library

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, and robot-robot + robot-environment collision checking. Robot models include (run with scripts/visualize_robot.py to view):

1. Franka Panda
2. Iiwa 7dof manipulator
3. Fetch 8dof mobile manipulator

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

1. forward_kinematics_batch(): batched forward kinematics
2. jacobian_batch_pt(): batched manipulator jacobian calculation (change of the end effector's pose w.r.t. each joint angle)
3. inverse_kinematics_single_step_levenburg_marquardt(): computes a single batched inverse kinematics step using Levenburg-Marquardt optimization
4. inverse_kinematics_single_step_batch_pt(): computes a single batched inverse kinematics step using the traditional jacobian pseudo-inverse method
5. self_collision_distances_batch(): batched self-collision distance calculation
6. self_collision_distances_jacobian_batch(): batched calculation of the jacobian of the pair-wise self-collision distances w.r.t. joint angles
7. env_collision_distances_batch(): batched self-environment distance calculation
8. env_collision_distances_jacobian_batch(): batched calculation of the jacobian of the self-environment distances w.r.t. joint angles

Quickstart code. This script 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_batch(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_single_step_levenburg_marquardt(poses, ik_sols)
assert_poses_almost_equal(poses, robot.forward_kinematics_batch(ik_sols))

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

Installation

Recommended: clone the repo and install with pip

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

Second option: Install from pypi (not recomended - the pypi version will likely be out of date until this project hardens)

pip install jrl