xiangshengcn's repositories
ApproximateNonlinearMPC
Implementation of model predictive control for nonlinear systems via machine learning and function approximators
Attitude-Optimal-Backstepping-Controller-Based-Quaternion-for-a-UAV
Implementation of Attitude Optimal Backstepping Controller for UAV
awesome-rl
Reinforcement learning resources curated
awesome-robotics-libraries
:sunglasses: A curated list of robotics libraries and software
deep-neuroevolution
Deep Neuroevolution
Deep-Reinforcement-Learning-Survey
My Exploration on Deep Reinforcement Learning Survey
gps
Guided Policy Search
ifopt
An Eigen-based, light-weight C++ Interface to Nonlinear Programming Solvers (Ipopt, Snopt)
mav_trajectory_generation
Polynomial trajectory generation and optimization, especially for rotary-wing MAVs.
moveit
The MoveIt! motion planning framework
movement_primitives_via_optimization
Implementation of the paper "Movement Primitives via Optimization" (Dragan et al., 2016). It includes both the adaptation of trajectories with DMP and learning a better adaptation norm.
Nurbs-surface-model
Representing curves and surfaces with NURBS model .
oh-distro
An integrated humanoid control, planning and perception system. Developed by MIT and the University of Edinburgh for the Boston Dynamics Atlas and the NASA Valkyrie humanoid robots
openai_lab
An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
openrave
Open Robotics Automation Virtual Environment: An environment for testing, developing, and deploying robotics motion planning algorithms.
pilco-matlab
PILCO policy search framework (Matlab version)
PythonRobotics
Python sample codes for robotics algorithms.
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Reinforcement-learning-Algorithms-and-Dynamic-Programming
Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value Function Approximation were applied to stabilize an inverted pendulum system and achieve optimal control. So essentially, the concept of Reinforcement Learning Controllers has been established. The Reinforcement Learning Controllers have been compared on the basis of performance and efficiency and they are separately compared with the classical Linear Quadratic Regulator Controller. Each of the RL controller have been integrated with a Swing up controller. A virtual switch toggles between the Swing up controller and the RL controller automatically, based on the value of the angular deviation theta with respect to the vertical plane. My research paper and my undergraduate thesis have been uploaded for reference. All the codes have also been uploaded.
softqlearning
Reinforcement Learning with Deep Energy-Based Policies
sparse_rrt
Python bindings for Sparse-RRT planner
TOPP
Time-Optimal Path Parameterization (à la Bobrow)
towr
TOWR - Trajectory Optimizer for Walking Robots
traj-gen-and-tracking
Trajectory generation and tracking for Table Tennis