puppersim

Simulation and Reinforcement Learning for DJI Pupper v2 robot

Conda setup link

Install conda, then

conda create --name rl_pupper python=3.7
conda activate rl_pupper
pip install ray arspb

Getting the code ready

git clone https://github.com/jietan/puppersim.git
cd puppersim
pip install -e .         (there is a dot at the end)
python3 puppersim/pupper_example.py       (this verifies the installation, you should see pybullet window show up with a pupper in it)

If pupper_example.py is running very slowly, try

python3 puppersim/pupper_minimal_server.py

then in a new terminal tab/window

python3 puppersim/pupper_example.py --render=False

This runs the visualizer GUI and simulator as two separate processes.

Training

ray start --head
python3 puppersim/pupper_ars_train.py --rollout_length=200
ray stop (after training is completed)

Test an ARS policy during training (file location may be different)

python3 puppersim/pupper_ars_run_policy.py  --expert_policy_file  data/lin_policy_plus_latest.npz  --json_file data/params.json --render

Prerequisites before deployment to Pupper

Set up Avahi (for linux only, one time only per computer)

sudo apt install avahi-* (one time)

Run the following, you should see ip address of pupper

avahi-resolve-host-name raspberrypi.local -4

Setup the zero password login for your pupper (Original password on raspberry pi: raspberry)

One time only per computer, run

ssh-keygen

One time only per pupper, run

Linux

cat ~/.ssh/id_rsa.pub | ssh pi@`avahi-resolve-host-name raspberrypi.local -4 | awk '{print $2}'` 'mkdir .ssh/ && cat >> .ssh/authorized_keys'

MacOs

cat ~/.ssh/id_rsa.pub | ssh pi@raspberrypi.local 'mkdir -p .ssh/ && cat >> .ssh/authorized_keys'

Run a pretrained policy on the Pupper robot

Turn on the Pupper robot, wait for it to complete the calibration motion.
Connect your laptop with the Pupper using an USB-C cable
Run the following command on your laptop:

./deploy_to_robot.sh python3 puppersim/puppersim/pupper_ars_run_policy.py --expert_policy_file=puppersim/data/lin_policy_plus_latest.npz --json_file=puppersim/data/params.json --run_on_robot

Using the heuristic control

Navigate to the outer puppersim folder and run

python3 puppersim/pupper_server.py

Clone the the heuristic controller:

git clone https://github.com/stanfordroboticsclub/StanfordQuadruped.git
cd StanfordQuadruped
git checkout dji

The dji branch is checked out so you can use the version of code for Pupper V2 rather than the servo-based Pupper V1.

In a separate terminal, navigate to StanfordQuadruped and run

python3 run_djipupper_sim.py

Keyboard controls:

wasd: left joystick --> moves robot forward/back and left/right
arrow keys: right joystick --> turns robot left/right
q: L1 --> activates/deactivates robot
e: R1 --> starts/stops trotting gait
ijkl: d-pad
x: X
square: u
triangle: t
circle: c

jakegrigsby/puppersim