dkt VS Unity-Robotics-Hub

Compare dkt vs Unity-Robotics-Hub and see what are their differences.

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dkt Unity-Robotics-Hub
1 12
257 1,889
- 2.2%
6.8 0.0
10 months ago about 1 month ago
Jupyter Notebook C#
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

dkt

Posts with mentions or reviews of dkt. We have used some of these posts to build our list of alternatives and similar projects.

Unity-Robotics-Hub

Posts with mentions or reviews of Unity-Robotics-Hub. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-01.

What are some alternatives?

When comparing dkt and Unity-Robotics-Hub you can also consider the following projects:

RigidBodySim.jl - Simulation and visualization of articulated rigid body systems in Julia

gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim

pink - Python inverse kinematics based on Pinocchio

ros-sharp - ROS# is a set of open source software libraries and tools in C# for communicating with ROS from .NET applications, in particular Unity3D

openbiomechanics - The open source initiative for anonymized, elite-level athletic motion capture data. Run by Driveline Baseball.

mediapipe - Cross-platform, customizable ML solutions for live and streaming media.

ROS-TCP-Connector

URDF-Importer - URDF importer

ZeroSimROSUnity - Robotic simulation in Unity with ROS integration.

moveit - :robot: The MoveIt motion planning framework

robotics-toolbox-python - Robotics Toolbox for Python

MuJoCo_RL_UR5 - A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.