nizhihao

Collaborative-Pushing-Grasping

deep-rl-grasping

Train deep reinforcement learning model for robotics grasping. Choose from different perception layers raw Depth, RGBD and autoencoder. Test the learned models in different scenes and object datasets

gym_gazebo_kinetic

Using Openai & ROS

pybullet-object-models

Collection of object models compatible with pybullet simulator https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet

PythonRobotics

Python sample codes for robotics algorithms.

pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

qhoenix

Flappy bird with Q-Learning on FPGA

Reach-Object

RoboNet

test

nizhihao/Collaborative-Pushing-Grasping

nizhihao/Reach-Object

nizhihao/deep-rl-grasping

Train deep reinforcement learning model for robotics grasping. Choose from different perception layers raw Depth, RGBD and autoencoder. Test the learned models in different scenes and object datasets

nizhihao/gym_gazebo_kinetic

Using Openai & ROS

nizhihao/pybullet-object-models

Collection of object models compatible with pybullet simulator https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet

nizhihao/PythonRobotics

Python sample codes for robotics algorithms.

nizhihao/pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

nizhihao/qhoenix

Flappy bird with Q-Learning on FPGA

nizhihao/RoboNet

nizhihao/test

nizhihao/uois

nizhihao/ycb-tools

Tools to download models from the YCB dataset and use them with the Gazebo simulator.