Lab 5 - Reinforcement Learning
Lecture 5.1: Intro to RL and Its Applications in Robotics
Intro to RL and Its Applications in Robotics Lecture by Tingnan Zhang, May 3, 2023.
Lecture 5.2: Reinforcement Learning on Legged Robots
Reinforcement learning on legged robots lecture by Jie Tan, May 10, 2023.
Lecture 5.3: Embodied Intelligence
Embodied Intelligence lecture by Adrien Gaidon, May 15, 2023.
Zoom password (if prompted): ^dgGqIB5
Lab instructions
These instructions assume you are running Mac or Linux. If you have Windows 10 or lower, I recommend dual-booting linux. If you have Windows 11, try using the Windows Linux Subsystem. Otherwise proceed at your own risk!
Step 1. Set up simulation environment
Clone the simulator repository
git clone https://github.com/jietan/puppersim.gitFollow the instructions in the System setup and Getting the code ready sections of the Puppersim README.md.
Step 2. Train the policy using RL (ARS)
Follow instructions at https://github.com/Nate711/puppersim/blob/main/puppersim/reacher/README.md to run the commands to train the policy.
Wait about 50 iterations until going to step 3 but leave it training
Step 3. Run your policy in simulator
Follow instructions at https://github.com/Nate711/puppersim/blob/main/puppersim/reacher/README.md to run the policy.
Step 4. Deploy policy to robot
Follow instructions at https://github.com/Nate711/puppersim/blob/main/puppersim/reacher/README.md to deploy to your robot.
Step 5. One day project of your choice
Do your own mini project!
Some ideas:
Teach the robot to trace out a specific shape in the air. (medium)
Teach the robot to turn itself off by pressing its power button. (medium)
Add a cube in the pybullet simulation and teach the robot to kick it. (hard)
Turn off torque on the elbow or shoulder motor and make the robot learn to balance the arm vertically. (hard)