Klamp't

Klamp't (Kris' Locomotion and Manipulation Planning Toolbox) is an open-source, cross-platform software package for robot modeling, simulating, planning, optimization, and visualization. It aims to provide an accessible, wide range of programming tools for learning robotics, analyzing robots, developing algorithms, and prototyping intelligent behaviors. It has particular strengths in robot manipulation and locomotion.

Historically, it began development at Indiana University since 2009 primarily as a research platform. Beginning in 2013 it has been used in education at Indiana University and Duke University. Since then, it has been adopted by other labs around the world.

More information can be found on the Klamp't website (http://klampt.org)

Features
Installation
Documentation
Version history
Who uses Klamp't?
Comparison to related packages
Contributors

Features

Unified C++ and Python package for robot modeling, kinematics, dynamics, control, motion planning, simulation, and visualization.
Supports legged and fixed-based robots.
Interoperable with Robot Operating System (ROS) and Open Motion Planning Library (OMPL).
Many sampling-based motion planners implemented.
Fast trajectory optimization routines.
Real-time motion planning routines.
Forward and inverse kinematics, forward and inverse dynamics
Contact mechanics computations (force closure, support polygons, stability of rigid bodies and actuated robots)
Planning models are fully decoupled from simulation models. This helps simulate uncertainty and modeling errors.
Robust rigid body simulation with triangle mesh / triangle mesh collisions.
Simulation of PID controlled, torque controlled, and velocity controlled motors.
Simulation of various sensors including cameras, depth sensors, laser range finders, gyroscopes, force/torque sensors, and accelerometers.
Works on several platforms:
- *nux environments
- Windows
- MacOS

(Please let us know if you are able to compile on other platforms in order to help us support them in the future.)

Installation

Quick start (Python API only):

pip install klampt

To run a visualization:

pip install PyOpenGL
pip install PyQt5
git clone http://github.com/krishauser/Klampt-examples (this is needed to run example programs)
cd Klampt-examples/Python/demos
python gl_vis.py

Installation instructions are also available for

Documentation

Python Manual and API Documentation

C++ Manual

API documentation is available here

Reporting bugs and getting help

If you identify a programming bug or issue, please raise them on this Github site. If you have general questions installing or programming with Klamp't, please ask them on the Klamp't forum, which is available on GitQ: https://gitq.com/krishauser/Klampt.

Version history

0.8.2 Latest version (11/15/2019)

Can now load almost all files from URLs! Just use an http:// prefix to the filenames of worlds, robots, rigid objects, or geometries. This also works for resources in Python. (Resources in C++ are not done yet)
New mesh visualization shows smooth meshes and black silhouettes by default, making things look prettier.
Can now mount robots in URDF files (see tutorial for an example).
Added ability to specify cost functions in kinematic planning.
Added conversions to Numpy objects in Python API (klampt.io.numpy_convert)
Added much more ROS support in Python API (klampt.io.ros module).
Added conversions to Open3D objects in Python API (klampt.io.open3d_convert)
The Python loading API has changed to have a more complete list of types and file extensions.
Fixed some bugs when editing in the klampt_browser Python program.
Fixed some motion planning bugs (closed loop + moving subset spaces in Python, some planners in C++).
Fixed a bizarre Python 2 to 3 conversion bug in se3.mul.

0.8.1 (1/3/2019)

Cleaned up documentation, separating C++ and Python docs. Sphinx is now used for Python docs.
Added klampt_path utility to Python API.
Added Python API bindings for higher order dynamics functions that were already in the C++ library (Cartesian accelerations, Hessians, derivatives of mass matrices).
Added rootfind Python C++ extension back.

0.8.0 (12/17/2018)

Cleaner file structure, with C++ files in the Cpp directory.
Improved build system for Python, allowing easy installation via pip install python.
Integration with Jupyter Notebook
Added Python utility programs (klampt_browse, klampt_sim, klampt_thumbnail).
Improvements support Python visualization on Mac.
Upgraded to PyQt5. PyQt4 is still supported for now.
Geometry conversions exposed in Python via the convert function.
Improved usage of some graphics card resources for streaming point clouds.
Support for LOG4CXX logging.
Removed dependencies on Boost and upgraded to C++11.
Removed dependencies on GLUT and GLUI. (Some examples still need to be upgraded to Qt.)
Cleaned up some cruft in KrisLibrary.

0.7 (3/24/2017)

Improved simulation stability, including adaptive time stepping and instability detection/recovery.
The proprietary .tri geometry file format has been replaced with the Object File Format (OFF) for better compatibility with 3D modeling packages.
Simulated visual, depth, and laser range sensors are now fully supported.
ROS sensor simulation broadcasting is enabled in Klampt/IO/ROS.h.
World XML files can now be saved to disk.
Robot sensors and controllers can be attached directly to a robot model using the sensors / controller properties in the robot's .rob or .urdf file.
The motion planning structure in KrisLibrary has been completely revamped in preparation for support of optimal and kinodynamic planning, but this should be a mostly transparent change to Klamp't users.
The Python interface is now better organized. However, the module structure is incompatible with code developed for versions 0.6.2 and earlier. In particular, math modules (vectorops, so3, se3) are now in the math subpackage, and visualization modules (glprogram, glrobotprogram, etc) are now in the vis subpackage.
Custom Python simulations of sensors, actuators, and force appliers that work on fast simulation rates are easier to integrate with slower control loops in the sim.simulation module.
Revamped and enhanced Python visualization functionality in the vis module. Multiple windows, simultaneous viewports, trajectory visualization, custom in-visualization plotting, automatic viewport determination, and thumbnail saving are now supported.
Cartesian trajectory generation, file loading utilities are added to Python.

0.6.2 (7/31/2016)

New Python APIs for visualization
Geometry caching helps load times and memory usage for large scenes
A global IK solver has been added to the Python API
ROS broadcasting / subscribing is enabled in the C++ API.

0.6.1 (3/21/2015)

Added functionality in Python API to load/save/edit resources, manipulate transforms and robot configurations via widgets, change appearance of objects, and run programs through Qt.
Removed the Python collide module. All prior functionality is now placed in the Geometry3D class in the standard klampt module.
Real-time planning interface has been greatly simplified.
The MilestonePathController class will be deprecated, use PolynomialPathController instead.
Minor bug fixes

0.6 (7/4/2014)

CMake build system makes it easier to build across multiple platforms
Easy connections with external controllers via ROS or a serial protocol
More user-friendly Qt application front ends
More demos, example code, and tutorials
Direct loading of URDF files with <klampt> XML tag
More calibrated robots: Baxter, RobotiQ 3-finger adaptive gripper
Unification of locomotion and manipulation via the GeneralizedRobot mechanism
Fixed build for Cygwin
More sophisticated logging capabilities in SimTest (contacts, commanded/actual/sensed paths)
Miscellaneous debugging throughout

0.5. Initial release (11/17/2013)

Who uses Klamp't?

(This is not an exhaustive list; if you are using Klampt and would like to be listed, let us know!)

Intelligent Motion Laboratory (IML), on the Baxter, TRINA, RoboSimian, and UR5 robots. Klamp't has also been used in dozens of academic papers.
Multiple IML open-source projects, including:
Duke courses (ECE 383, ECE 489)
Team Duke in the Amazon Picking Challenge 2016-2018.
SimGrasp at Stanford University
Research labs at Brown, RPI, Columbia, and IIT Pisa
TeamHubo in the DARPA Robotics Challenge
IROS 2016 Manipulation Challenge, Simulation Track

Comparison to related packages

ROS (Robot Operating System) is a middleware system designed for distributed control of physical robots, and Klamp't is designed to be interoperable with it. Various ROS software packages can replicate many of the functions of Klamp't when used together (Gazebo, KDE, Rviz, MoveIt!), but this approach is difficult since these tools are not as tightly integrated as they are in Klamp't. ROS has limited support for legged robots, and is poorly suited for prototyping high-rate feedback control systems. ROS is heavy-weight, has a steep learning curve especially for non-CS students, and is also not completely cross-platform (only Ubuntu is fully supported).
OpenRAVE (Robotics and Animation Virtual Environment) is similar to Klamp't and was developed concurrently by a similar group at CMU. OpenRAVE has more sophisticated manipulation planning functionality. Does not support planning for legged robots, but simulation is possible with some effort. Simulation models are often conflated with planning models whereas in Klamp't they are fully decoupled. OpenRAVE is no longer actively supported.
Gazebo, Webots, V-REP, etc are robot simulation packages built off of the same class of rigid body simulations as Klamp't. They have more sophisticated sensor simulation capabilities, cleaner APIs, and nicer visualizations but are typically built for mobile robots and have limited functionality for modeling, planning, and optimization. Klamp't also has improved mesh-mesh collision handling that makes collision handling much more stable.

Contributors

Kris Hauser has been the primary maintainer throughout the project. Other major contributors include Jordan Tritell, Jingru Luo, and Alessio Rocchi.

Adam Konnecker, Cam Allen, and Steve Kuznetsov have helped with the Mac build. Hayden Bader helped with the prebuilt Docker container.

As an open-source project, we welcome contributions and suggestions from the community.

Yeonju-Kim / Klampt