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A Python library for robotic education and research

Project description

Robotics Toolbox for Python

PyPI version Anaconda version PyPI - Python Version License: MIT Binder QUT Centre for Robotics Open Source

Build Status Coverage Language grade: Python PyPI - Downloads

A Python implementation of the Robotics Toolbox for MATLAB®


This toolbox brings robotics-specific functionality to Python, and leverages Python's advantages of portability, ubiquity and support, and the capability of the open-source ecosystem for linear algebra (numpy, scipy), graphics (matplotlib, three.js, WebGL), interactive development (jupyter, jupyterlab,, and documentation (sphinx).

The Toolbox provides tools for representing the kinematics and dynamics of serial-link manipulators - you can easily create your own in Denavit-Hartenberg form, import a URDF file, or use over 30 supplied models for well-known contemporary robots from Franka-Emika, Kinova, Universal Robotics, Rethink as well as classical robots such as the Puma 560 and the Stanford arm.

The toolbox will also support mobile robots with functions for robot motion models (unicycle, bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF).

The Toolbox provides:

  • code that is mature and provides a point of comparison for other implementations of the same algorithms;
  • routines which are generally written in a straightforward manner which allows for easy understanding, perhaps at the expense of computational efficiency;
  • source code which can be read for learning and teaching;
  • backward compatability with the Robotics Toolbox for MATLAB

The Toolbox leverages the Spatial Maths Toolbox for Python to provide support for data types such as SO(n) and SE(n) matrices, quaternions, twists and spatial vectors.

Code Example

We will load a model of the Franka-Emika Panda robot defined classically using modified (Craig's convention) Denavit-Hartenberg notation

import roboticstoolbox as rtb
robot = rtb.models.DH.Panda()

	Panda (by Franka Emika): 7 axes (RRRRRRR), modified DH parameters
	 aⱼ₋₁     ₋₁   θⱼ    dⱼ      q       q   
	    0.0    0.0°   q1  0.333  -166.0°  166.0° 
	    0.0  -90.0°   q2    0.0  -101.0°  101.0° 
	    0.0   90.0°   q3  0.316  -166.0°  166.0° 
	 0.0825   90.0°   q4    0.0  -176.0°   -4.0° 
	-0.0825  -90.0°   q5  0.384  -166.0°  166.0° 
	    0.0   90.0°   q6    0.0    -1.0°  215.0° 
	  0.088   90.0°   q7  0.107  -166.0°  166.0° 

	tool  t = 0, 0, 0.1; rpy/xyz = -45°, 0°, 0° 

	name  q0   q1      q2   q3     q4   q5     q6   
	  qz   0°   0°      0°   0°     0°   0°     0°  
	  qr   0°  -17.2°   0°  -126°   0°   115°   45° 

T = robot.fkine(robot.qz)  # forward kinematics

	   0.707107    0.707107    0           0.088
	   0.707107   -0.707107    0           0
	   0           0          -1           0.823
	   0           0           0           1

(Python prompts are not shown to make it easy to copy+paste the code, console output is indented)

We can solve inverse kinematics very easily. We first choose an SE(3) pose defined in terms of position and orientation (end-effector z-axis down (A=-Z) and finger orientation parallel to y-axis (O=+Y)).

from spatialmath import SE3

T = SE3(0.7, 0.2, 0.1) * SE3.OA([0, 1, 0], [0, 0, -1])
sol = robot.ikine_LM(T)         # solve IK
	IKsolution(q=array([  0.2134,    1.867,  -0.2264,   0.4825,   0.2198,    1.396,   -2.037]), success=True, reason=None, iterations=12, residual=1.4517646473808178e-11)

q_pickup = sol.q
print(robot.fkine(q_pickup))    # FK shows that desired end-effector pose was achieved

		-1            9.43001e-14  2.43909e-12  0.7
		 9.43759e-14  1            7.2574e-13   0.2
		-2.43913e-12  7.2575e-13  -1            0.1
		 0            0            0            1

Note that because this robot is redundant we don't have any control over the arm configuration apart from end-effector pose, ie. we can't control the elbow height.

We can animate a path from the upright qz configuration to this pickup configuration

qt = rtb.jtraj(robot.qz, q_pickup, 50)
robot.plot(qt.q, movie='panda1.gif')

Panda trajectory animation

which uses the default matplotlib backend. Grey arrows show the joint axes and the colored frame shows the end-effector pose.

Let's now load a URDF model of the same robot. The kinematic representation is no longer based on Denavit-Hartenberg parameters, it is now a rigid-body tree.

robot = rtb.models.URDF.Panda()  # load URDF version of the Panda
print(robot)    # display the model

	panda (by Franka Emika): 7 axes (RRRRRRR), ETS model
	id      link        parent        joint                                          ETS                                      
	 0   panda_link0          _O_                {panda_link0} = {_O_}                                                        
	 1   panda_link1  panda_link0  panda_joint1  {panda_link1} = {panda_link0}  * tz(0.333) * Rz(q0)                          
	 2   panda_link2  panda_link1  panda_joint2  {panda_link2} = {panda_link1}  * Rx(-90°) * Rz(q1)                           
	 3   panda_link3  panda_link2  panda_joint3  {panda_link3} = {panda_link2}  * ty(-0.316) * Rx(90°) * Rz(q2)               
	 4   panda_link4  panda_link3  panda_joint4  {panda_link4} = {panda_link3}  * tx(0.0825) * Rx(90°) * Rz(q3)               
	 5   panda_link5  panda_link4  panda_joint5  {panda_link5} = {panda_link4}  * tx(-0.0825) * ty(0.384) * Rx(-90°) * Rz(q4) 
	 6   panda_link6  panda_link5  panda_joint6  {panda_link6} = {panda_link5}  * Rx(90°) * Rz(q5)                            
	 7   panda_link7  panda_link6  panda_joint7  {panda_link7} = {panda_link6}  * tx(0.088) * Rx(90°) * Rz(q6)                
	 8  @panda_link8  panda_link7  panda_joint8  {panda_link8} = {panda_link7}  * tz(0.107)                                   

	name  q0   q1      q2   q3     q4   q5     q6   
	  qz   0°   0°      0°   0°     0°   0°     0°  
	  qr   0°  -17.2°   0°  -126°   0°   115°   45° 

The symbol @ indicates the link as an end-effector, a leaf node in the rigid-body tree.

We can instantiate our robot inside a browser-based 3d-simulation environment.

from roboticstoolbox.backends.Swift import Swift  # instantiate 3D browser-based visualizer
backend = Swift()
backend.launch()            # activate it
backend.add(robot)          # add robot to the 3D scene
for qk in qt.q:             # for each joint configuration on trajectory
      robot.q = qk          # update the robot state
      backend.step()        # update visualization

Getting going


You will need Python >= 3.6

Using pip

Install a snapshot from PyPI

pip3 install roboticstoolbox-python

Available options are:

  • vpython install VPython backend
  • collision install collision checking with pybullet

Put the options in a comma separated list like

pip3 install roboticstoolbox-python[optionlist]

Swift, a web-based visualizer, is installed as part of Robotics Toolbox.

From GitHub

To install the bleeding-edge version from GitHub

git clone
cd robotics-toolbox-python
pip3 install -e .

Run some examples

The notebooks folder contains some tutorial Jupyter notebooks which you can browse on GitHub.

Or you can run them, and experiment with them, at

Toolbox Research Applications

The toolbox is incredibly useful for developing and prototyping algorithms for research, thanks to the exhaustive set of well documented and mature robotic functions exposed through clean and painless APIs. Additionally, the ease at which a user can visualize their algorithm supports a rapid prototyping paradigm.

Check out our ICRA 2021 paper on IEEE Xplore or get the PDF from Peter's website.

If the toolbox helped you in your research, please cite

  title={Not your grandmother’s toolbox--the Robotics Toolbox reinvented for Python},
  author={Corke, Peter and Haviland, Jesse},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},

Publication List

J. Haviland, N. Sünderhauf and P. Corke, "A Holistic Approach to Reactive Mobile Manipulation," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2022.3146554. In the video, the robot is controlled using the Robotics toolbox for Python and features a recording from the Swift Simulator.

[Arxiv Paper] [IEEE Xplore] [Project Website] [Video] [Code Example]

J. Haviland and P. Corke, "NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2021.3056060. In the video, the robot is controlled using the Robotics toolbox for Python and features a recording from the Swift Simulator.

[Arxiv Paper] [IEEE Xplore] [Project Website] [Video] [Code Example]

A Purely-Reactive Manipulability-Maximising Motion Controller, J. Haviland and P. Corke. In the video, the robot is controlled using the Robotics toolbox for Python.

[Paper] [Project Website] [Video] [Code Example]

Common Issues

See the common issues with fixes here.

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