| ahundt/awesome-robotics |
817 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
|
|
| A curated list of awesome links and software libraries that are useful for robots. |
| ahundt/robotics_setup |
112 |
|
0 |
0 |
almost 5 years ago |
0 |
|
1 |
apache-2.0 |
Shell |
| Setup Ubuntu 18.04, 16.04 and 14.04 with machine learning and robotics software plus user configuration. Includes ceres tensorflow ros caffe vrep eigen cudnn and cuda plus many more. |
| jhu-lcsr/costar_plan |
49 |
|
0 |
0 |
over 6 years ago |
0 |
|
35 |
apache-2.0 |
Python |
| Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support. |
| micah5/totally_humans |
24 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
|
Python |
| rnn trained on r/totallynotrobots 🤖 |
| CedarStreetGarage/python-keras-neural-network-inverse-kinematics |
18 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Training networks to compute forward and inverse kinematics - more generally, nonlinear function estimation with potential singularities |
| greerviau/BetterRobotIdeas |
14 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
mit |
Python |
| A Keras implementation of an LSTM that generates new robot idea comments for Michael Reeves |
| Suman7495/rl-botics |
10 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
mit |
Python |
| Deep Reinforcement Learning Toolbox for Robotics using Keras and TensorFlow |
| akshay1997feb/Self-Driving-Robot-Using-Neural-Network |
9 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
mit |
Python |
| This project introduces the autonomous robot which is a scaled down version of actual self-driving vehicle and designed with the help of neural network. The main focus is on building autonomous robot and train it on a designed track with the help of neural network so that it can run autonomously without a controller or driver on that specific track. The robot will stream the video to laptop which will then take decisions and send the data to raspberry pi which will then control the robot using motor driver. This motor driver will move the robot in required directions. Neural Network is used to train the model by first driving the robot on the specially designed track by labeling the images with the directions to be taken. After the model is trained it can make accurate predictions by processing the images on computer. This approach is better than conventional method which is done by extracting specific feature from images. |
| sahouston/Cozmo-Autonomous-Driving |
8 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
mit |
Python |
| Machine Learning for Cozmo Robot Autonomous Driving |
| yans0ng/Spyndra-Gait-Learning |
6 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Autonomous gait generation of quadruped robot |
