CRAM
A framework for cognitive robot abstract architectures to perform high-level tasks.
A framework for cognitive robot abstract architectures to perform high-level tasks.
PyCRAM is a toolbox for designing, implementing and deploying software on autonomous robots. The framework provides various tools and libraries for aiding in robot software development as well as geometric reasoning and fast simulation mechanisms to develop cognition-enabled control programs that achieve high levels of robot autonomy.
A knowledge-processing system for autonomous robots that integrates symbolic and geometric reasoning.
An open-source platform for task planning, execution, and monitoring in robotic systems.
A powerful framework for motion planning and control in high-dimensional robotic spaces.
Joint Probability Trees (short JPTs) are a formalism for learning of and reasoning about joint probability distributions, which is tractable for practical applications.
The Probalistic Model package contains fast and flexible implementations for various probabilistic models. This package provides a clean, unifying and well documented API to probabilistic models. Just like sklearn does for classical machine learning models.
The package is designed to provide a simple and flexible way to generate events that are suitable for probabilistic reasoning in a python.