Robotics Toolbox for Python

Robotics without the cruft
A high-productivity framework for robotics research and education.

GitHub • Wiki • Changelog • Installation

Status & Project Health

PyPI - Python Version

Ecosystem & Dependencies

Synopsis
Getting going
Tutorials
Code Examples
Toolbox Research Applications
References
Using the Toolbox in your Open Source Code?
Common Issues and Solutions
Build a JupyterLite/Pyodide Wasm wheel

Synopsis

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, mybinder.org), 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 50 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 contains fast implementations of kinematic operations. The forward kinematics and the manipulator Jacobian can be computed in less than 1 microsecond while numerical inverse kinematics can be solved in as little as 4 microseconds.

The toolbox also supports 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.

Getting going

You will need Python >= 3.10

Using pip

Install a snapshot from PyPI

pip install roboticstoolbox-python

Available options are:

swift install Swift, a web-based visualizer
qp install quadratic-programming IK dependencies (qpsolvers, quadprog)
collision install collision checking with coal and trimesh
tool install IPython and pygments, needed to run the rtbtool interactive shell
all install swift, qp, collision, and tool

Windows note: coal does not publish Windows wheels on PyPI, so the collision/all extras skip it there and collision checking is unavailable via pip on Windows. It's available via conda install -c conda-forge coal-python if needed. Everything else in the Toolbox works normally.

Put the options in a comma separated list like

pip install roboticstoolbox-python[optionlist]

If you want the Swift visualizer, install the swift extra.

Install matrix:

Core only

pip install roboticstoolbox-python

Swift visualizer only

pip install roboticstoolbox-python[swift]

QP solver dependencies only

pip install roboticstoolbox-python[qp]

Collision checking dependencies only

pip install roboticstoolbox-python[collision]

rtbtool interactive shell dependencies only

pip install roboticstoolbox-python[tool]

Everything (swift + qp + collision + tool)

pip install roboticstoolbox-python[all]

Multiple extras explicitly

pip install roboticstoolbox-python[swift,qp,collision]

From GitHub

To install the bleeding-edge version from GitHub

git clone https://github.com/petercorke/robotics-toolbox-python.git
cd robotics-toolbox-python
pip install -e .

To generate a Wasm wheel that will run in the browser see the instructions here.

Tutorials

Do you want to learn about manipulator kinematics, differential kinematics, inverse-kinematics and motion control? Have a look at our tutorial. This tutorial comes with two articles to cover the theory and 12 Jupyter Notebooks providing full code implementations and examples. Most of the Notebooks are also Google Colab compatible allowing them to run online.

Code Examples

We will load a model of the Franka-Emika Panda robot defined by a URDF file

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

	ERobot: panda (by Franka Emika), 7 joints (RRRRRRR), 1 gripper, geometry, collision
	┌─────┬──────────────┬───────┬─────────────┬────────────────────────────────────────────────┐
	│link │     link     │ joint │   parent    │              ETS: parent to link               │
	├─────┼──────────────┼───────┼─────────────┼────────────────────────────────────────────────┤
	│   0 │ panda_link0  │       │ BASE        │                                                │
	│   1 │ panda_link1  │     0 │ panda_link0 │ SE3(0, 0, 0.333) ⊕ Rz(q0)                      │
	│   2 │ panda_link2  │     1 │ panda_link1 │ SE3(-90°, -0°, 0°) ⊕ Rz(q1)                    │
	│   3 │ panda_link3  │     2 │ panda_link2 │ SE3(0, -0.316, 0; 90°, -0°, 0°) ⊕ Rz(q2)       │
	│   4 │ panda_link4  │     3 │ panda_link3 │ SE3(0.0825, 0, 0; 90°, -0°, 0°) ⊕ Rz(q3)       │
	│   5 │ panda_link5  │     4 │ panda_link4 │ SE3(-0.0825, 0.384, 0; -90°, -0°, 0°) ⊕ Rz(q4) │
	│   6 │ panda_link6  │     5 │ panda_link5 │ SE3(90°, -0°, 0°) ⊕ Rz(q5)                     │
	│   7 │ panda_link7  │     6 │ panda_link6 │ SE3(0.088, 0, 0; 90°, -0°, 0°) ⊕ Rz(q6)        │
	│   8 │ @panda_link8 │       │ panda_link7 │ SE3(0, 0, 0.107)                               │
	└─────┴──────────────┴───────┴─────────────┴────────────────────────────────────────────────┘

	┌─────┬─────┬────────┬─────┬───────┬─────┬───────┬──────┐
	│name │ q0  │ q1     │ q2  │ q3    │ q4  │ q5    │ q6   │
	├─────┼─────┼────────┼─────┼───────┼─────┼───────┼──────┤
	│  qr │  0° │ -17.2° │  0° │ -126° │  0° │  115° │  45° │
	│  qz │  0° │  0°    │  0° │  0°   │  0° │  0°   │  0°  │
	└─────┴─────┴────────┴─────┴───────┴─────┴───────┴──────┘

The symbol @ indicates the link as an end-effector, a leaf node in the rigid-body tree (Python prompts are not shown to make it easy to copy+paste the code, console output is indented). We will compute the forward kinematics next

Te = robot.fkine(robot.qr)  # forward kinematics
print(Te)

	0.995     0         0.09983   0.484
	0        -1         0         0
	0.09983   0        -0.995     0.4126
	0         0         0         1

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

Tep = SE3.Trans(0.6, -0.3, 0.1) * SE3.OA([0, 1, 0], [0, 0, -1])
sol = robot.ik_LM(Tep)         # solve IK
print(sol)

	(array([ 0.20592815,  0.86609481, -0.79473206, -1.68254794,  0.74872915,
			2.21764746, -0.10255606]), 1, 114, 7, 2.890164057230228e-07)

q_pickup = sol[0]
print(robot.fkine(q_pickup))    # FK shows that desired end-effector pose was achieved

	 1         -8.913e-05  -0.0003334  0.5996
	-8.929e-05 -1          -0.0004912 -0.2998
	-0.0003334  0.0004912  -1          0.1001
	 0          0           0          1

We can animate a path from the ready pose qr configuration to this pickup configuration

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

where we have specified the matplotlib pyplot backend. Blue arrows show the joint axes and the coloured frame shows the end-effector pose.

We can also plot the trajectory in the Swift simulator (a browser-based 3d-simulation environment built to work with the Toolbox)

robot.plot(qt.q)

We can also experiment with velocity controllers in Swift. Here is a resolved-rate motion control example

import swift
import roboticstoolbox as rtb
import spatialmath as sm
import numpy as np

env = swift.Swift()
env.launch(realtime=True)

panda = rtb.models.Panda()
handle = env.add(panda)
handle.q = panda.qr

Tep = panda.fkine(handle.q) * sm.SE3.Trans(0.2, 0.2, 0.45)

arrived = False

dt = 0.05

while not arrived:

    v, arrived = rtb.p_servo(panda.fkine(handle.q), Tep, 1)
    handle.qd = np.linalg.pinv(panda.jacobe(handle.q)) @ v
    env.step(dt)

# Uncomment to stop the browser tab from closing
# env.hold()

env.add(panda) returns a handle owning this instance's live joint state -- drive the simulation via handle.q/handle.qd, not panda.q/panda.qd directly. panda itself stays a plain, shareable kinematic model, so the same panda object can back several independent handles/instances at once.

Loading models from robot_descriptions

The Panda example above uses one of the ~50 models shipped with the Toolbox, but many more robots are available on demand via the robot_descriptions package, which is installed automatically as a dependency. Passing a bare name (no file suffix) to URDFRobot fetches and loads it from there:

from roboticstoolbox.models.URDF.URDFRobot import URDFRobot
ur5 = URDFRobot("ur5")

rtb.models.catalog() lists everything available — the models built into the Toolbox as well as those it can load from robot_descriptions — with filtering by keyword/DoF/model type and column sorting:

rtb.models.catalog(mtype="URDF", sorton="name")

Run some examples

The notebooks folder contains some tutorial Jupyter notebooks which you can browse on GitHub. Additionally, have a look in the examples folder for many ready to run examples.

References

Key papers

P. Corke, "A computer tool for simulation and analysis: the Robotics Toolbox for MATLAB," Proc. National Conf. Australian Robot Association, pp. 319–330, Melbourne, July 1995. [PDF]
P. Corke, "A robotics toolbox for MATLAB," IEEE Robotics and Automation Magazine, 3(1):24–32, Sept. 1996. [IEEE Xplore]
P. Corke, "A simple and systematic approach to assigning Denavit-Hartenberg parameters," IEEE Transactions on Robotics, 23(3):590–594, 2007. [IEEE Xplore] — introduces the Elementary Transform Sequence (ETS) notation used throughout the Toolbox.
J. Haviland and P. Corke, "A systematic approach to computing the manipulator Jacobian and Hessian using the elementary transform sequence," arXiv preprint, 2020. [arXiv]
P. Corke and J. Haviland, "Not your grandmother's toolbox – the Robotics Toolbox reinvented for Python," Proc. ICRA 2021. [IEEE Xplore] [PDF]

Talks

Peter Corke – The Robotics Toolbox: 30 Years Old and Still Going Strong — a retrospective on the Toolbox's history and motivation, a good starting point before diving into the API.

Related book

The Toolbox is a companion to Peter Corke's textbook Robotics, Vision & Control (Springer) — many docstrings and examples reference specific figures/sections from the book.

Citing the Toolbox

If the toolbox helped you in your research, please cite

@inproceedings{rtb,
  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)},
  pages={11357--11363},
  year={2021},
  organization={IEEE}
}

Using the Toolbox in your Open Source Code?

If you are using the Toolbox in your open source code, feel free to add our badge to your readme!

For the powered by robotics toolbox badge

copy the following

[![Powered by the Robotics Toolbox](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/rtb_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python)

For the powered by python robotics badge

copy the following

[![Powered by Python Robotics](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/pr_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python)

Common Issues and Solutions

See the common issues with fixes here.

Using the Toolbox with Windows?

Graphical visualisation via Swift is currently not supported under Windows. However there is a hotfix, by changing in SwiftRoute.py

self.path[9:] to self.path[10:]

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.

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]

K. He, R. Newbury, T. Tran, J. Haviland, B. Burgess-Limerick, D. Kulić, P. Corke, A. Cosgun, "Visibility Maximization Controller for Robotic Manipulation", in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2022.3188430. 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]

Build a JupyterLite/Pyodide Wasm wheel

Pyodide is a full CPython distribution compiled to WebAssembly, which is what lets the "Try it Now" JupyterLite deployment above run this toolbox entirely client-side, no server required. Each Pyodide release embeds one specific CPython version, and a wasm wheel is tagged with the CPython version it was built for (cp312, cp313, ...) -— a wheel only loads if its tag matches the CPython embedded in the Pyodide runtime actually running. JupyterLite doesn't bundle Pyodide directly either: the jupyterlite-pyodide-kernel package pulls in a specific Pyodide version per its own release, so bumping that one package can silently change which wheel tag your deployment now needs -— this is the single sharpest edge in this whole pipeline. This repo's live deployment currently pins jupyterlite-pyodide-kernel==0.6.1 (see .github/workflows/ci.yml), which embeds Pyodide 0.27.6 / CPython 3.12 — hence cp312 below.

Also note Pyodide's own version numbering changed in 2026: releases up to 0.29.x used an independent 0.x scheme, but from Pyodide 314.0.0 onward the version number tracks the embedded CPython version directly (314 = Python 3.14). "Current" no longer means a 0.x version.

Use the published wheel (recommended)

PyPI rejects the pyodide_* platform tag, so Wasm wheels can't be published there -— instead, every GitHub release attaches ready-built wheels (for each supported CPython version) as release assets. Download the one matching your deployment's CPython version, e.g.:

gh release download --repo petercorke/robotics-toolbox-python --pattern '*cp312*pyodide*'

This is exactly what ci.yml's docs-build job does to populate the live "Try it Now" site — most people should do this rather than building locally.

Build locally

Only needed to test an unreleased change, or to target a different CPython/Pyodide pin than the current release. Uses cibuildwheel's Pyodide platform:

make wheel-pyodide

Optionally pin the Pyodide runtime to match a different JupyterLite deployment than this project's own:

PYODIDE_VERSION=0.27.6 make wheel-pyodide

The target writes to dist/ and runs make wheel-pyodide-check, which validates the wheel filename contains:

cp312-cp312
wasm32
pyemscripten_<major>_<minor> or pyodide_<major>_<minor>

To inspect the produced artifact path:

ls -1 dist/*wasm32*.whl

References

Pyodide release notes — confirms what CPython version a given Pyodide tag embeds, including the 2026 versioning-scheme change.
jupyterlite-pyodide-kernel changelog — the authoritative source for which Pyodide version a given kernel package release bundles.
cibuildwheel Pyodide platform docs.

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SHA256	`7a0364e8f053f00ef6af9d03360dcb84300e61ca6d4943c2f19eb6c1a77bf13c`
MD5	`ce4d2c38b0c89a6455dfe8d05d33343e`
BLAKE2b-256	`507f274edfff2518436dc45b09f63895e501b5febc3e958cc81cb9c3c59a607c`

Algorithm	Hash digest
SHA256	`313d21fb438caf398d351780292864aa4bc4f6da375ef9f712309a59c4e82fc7`
MD5	`9b969de5c43d597eafc48b2e7c354a71`
BLAKE2b-256	`64beca1b3def4c7ecebee2977c850246dc84baf620f4851663d80a4375573097`

Algorithm	Hash digest
SHA256	`c882f5ebbb32904f05c21b2faa31a6e405993d90e3751bf387166cd21aab1180`
MD5	`4259000f4856cc24b669937a5976f2bc`
BLAKE2b-256	`40433b015036b8b7651c5c62a8fed071d45d62b5170a96e095f1ed5cb30539b9`

Algorithm	Hash digest
SHA256	`feb15e8f857679843c40f0b24395eea91bcc3c462a88369417e17e3d367c029f`
MD5	`901c08db822f1ee2477d81fd3e081b75`
BLAKE2b-256	`317e83bf52168a657bc17e8473a0da34ff43519efd027eb8da838f403c94d327`

Algorithm	Hash digest
SHA256	`f54f00e6a75baa340646b77a29aa6fc4eecb75c3182f2951b7f85d20f4ae7872`
MD5	`84c3baee93d2e53a3f697dde50fab7bf`
BLAKE2b-256	`53e84da6749c3b4111674b0933b1e847f15443ff2bbc574cb8967ed2ada293a1`

Algorithm	Hash digest
SHA256	`d4405b9015f017df08e17eecfb89775a414fedf989308703594c2f6a5567493f`
MD5	`2d2fcb2a2238fa6ed930f4e1e80ae275`
BLAKE2b-256	`96324089abe08f4b52888ca0e44f5cf15514022b9ec637641c3a235cfc5dea49`

Algorithm	Hash digest
SHA256	`96aad2c14dd8579e32cab21438257768f46435a8c842d64ca4dc5d130aea47a4`
MD5	`52bfe2c2c62cdc629df346c879d73d53`
BLAKE2b-256	`c242353ae88bebbfd0837eed04e706c715d2aab66b5ca7b7c80264a107a87653`

Algorithm	Hash digest
SHA256	`a4bd29afbf6e74c3cb492d00c514d2ccdf712cba14b53b6095127b49b1217c12`
MD5	`45a5ac24bc77af08bda9529750878120`
BLAKE2b-256	`955822d42114f79cab4c447be0773ce69a4a8bc7a9ccd2e01cbea0707d018c4c`

Algorithm	Hash digest
SHA256	`7a768a897711b9f3942f00a8f8354bcd3f63f7f4ae22a22cf9aad7eb7f557de5`
MD5	`0db15d3851905da676d281d35a8fdb4e`
BLAKE2b-256	`738a48e050d15897665fe7f388a9436ffceb7371d0603773c783543ec9326d10`

Algorithm	Hash digest
SHA256	`0b16afb14d92639688549d17b2b852645ef3d29953df2235d47415f758f517e5`
MD5	`79089b619795d94f41e11026e7a9f450`
BLAKE2b-256	`3bb0d5e9949b3691cc07a7f12c859372a49176def2aa0abbe3a9c6ea9a8a4fd8`

Algorithm	Hash digest
SHA256	`e7b7040c6f9a2a47bf51e2e3790c9c6c647742e36360ceb93153de57602bd49b`
MD5	`b51518ad3ad525c9483ca020f2494578`
BLAKE2b-256	`d3ea7a638fe7618f16ddcba168fb40ab88c61b9bf0f89be778f46fba8dc2643e`

Algorithm	Hash digest
SHA256	`75c40e878f07c9dcb1908d165beb8e145c644207f4ca3329c2d12df821c992e2`
MD5	`62f122fd13118f8c75e30b7111e30218`
BLAKE2b-256	`81d19e5b2cd2de3a41d678e31b11ea7b90b821970e03cffadf35b10a36c6975d`

roboticstoolbox-python 1.4.2

Robotics Toolbox for Python

Status & Project Health

Ecosystem & Dependencies

Contents

Synopsis

Getting going

Using pip

From GitHub

Tutorials

Code Examples

Loading models from robot_descriptions

Run some examples

References

Key papers

Talks

Related book

Citing the Toolbox

Using the Toolbox in your Open Source Code?

Common Issues and Solutions

Using the Toolbox with Windows?

Toolbox Research Applications

Publication List

Build a JupyterLite/Pyodide Wasm wheel

Use the published wheel (recommended)

Build locally

References

Project links

Key dates

2 maintainers

Credits

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

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Algorithm	Hash digest
SHA256	`d9c2a8e9f7c37eb4d2ee32b16d8f07ab667776478119c94c7b4ab1cb30590c53`
MD5	`be506dca7221e52900695b36fa297734`
BLAKE2b-256	`20a48742b0b56b2043e32ea6c312f7328347b6a6ccad4df453d06e55a020dbda`