Set up :

We will use gymnasium for our RL based learning and mujoco as the physics simulator/environment.

pip3 install mujoco pip3 install gymnasium

git clone https://github.com/WillMandil001/RL_Franka_Pushing.git

Then you need to build the custom environment (INB01014-v0)

pip3 install -e gym-INB0104

Running the current simulation:

cd src python3 gym-env-test.py

editing and adding to the simulation:

The Gymnasium RL training script is found at /src/gym-env-test.py
The gymnasium simulation environment is found in /gym-INB0104/envs/INB0104
The INB0104 .xml head script is found in /environments/INB0104/Robot_C.xml

ToDo:

we need to set up the reward to be correct.
we need to set up the DeepRL velocity controller. (camera RGB image as input)
Train the DeepRL policy
implement DeepRL network on the real robot and setup real world velocity controller
add safety control to the real world controller
test the system and asses sim-2-real gap for the camera and the robot.
implement Real World fine-tuning.