add intro to new embodiments

CONTRIBUTING.md

@@ -9,14 +9,6 @@ an issue or you want to add an enhancement.
 
 When creating a new pull request please add the issue number.
 
-## Adding new robot
-Adding new embodiment is very straightforward:
-1. Create a folder with a new robot [here](https://github.com/kscalelabs/sim/tree/master/sim).
-2. Add joint.py file setting up basic properties and join configuration - see an [example](https://github.com/kscalelabs/sim/blob/master/sim/resources/stompymini/joints.py).
-3. Add the new embodiment configuration and environment [here](https://github.com/kscalelabs/sim/tree/master/sim/envs).
-4. Add the new embodiment to the [registry](https://github.com/kscalelabs/sim/blob/master/sim/envs/__init__.py).
-
-We set up the logic so that your new robot should start to walk with basic configuration.
 
 ## Lint and formatting
 When you submit the PR we automatically run some checks using black, ruff and mypy.
@@ -26,3 +18,42 @@ You can locally run commands below to test the formatting:
 make format
 make static-checks
 ```
+
+## Adding a new robot
+Creating a new embodiment is quite straightforward. The best way is to copy an existing robot and modify it.
+The best starting point would be [stompymini](https://github.com/kscalelabs/sim/tree/master/sim/resources/stompymini).
+1. Create a folder with a new robot [here](https://github.com/kscalelabs/sim/tree/master/sim/resources).
+2. Add joint.py file setting up basic properties and joint configuration - see an [example](https://github.com/kscalelabs/sim/blob/master/sim/resources/stompymini/joints.py).
+3. Update [height](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/resources/stompymini/joints.py#L102) and default rotation of your humanoid.
+4. Add a new embodiment configuration and environment [here](https://github.com/kscalelabs/sim/tree/master/sim/envs/humanoids).
+5. Add a new embodiment to the [registry](https://github.com/kscalelabs/sim/blob/master/sim/envs/__init__.py).
+To run things export your default path
+```
+export MODEL_DIR=sim/resources/NEW_HUMANOID
+```
+and kick off the training
+```
+python sim/train.py --env-id NEW_HUMANOID --num-envs 4
+```
+
+## Making it stand
+1. The best way to start is to make your new robot stand. In this case you want to comment out
+the rewards that are responsible for making the robot walk. See an example of a [reward config](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/envs/humanoids/stompymini_config.py#L163).
+2. If the robot flies away, inspect your joint limits. During training, we introduce a lot of noise to the default joint positions
+as well as masses. You might need either adapt the limits or the [noise level](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/envs/humanoids/stompy_config.py#L213).
+3. Isaac Sim often hits velocities nans when some joints are hitting their limits - you can change the [engine parameters](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/envs/humanoids/stompy_config.py#L108) to fix this issue.
+4. The revolute joints cannot have 0 velocity in the URDF definition - otherwise, engine will go nans as well.
+5. Observe the reward for orientation and default joint position. The model should just farm these two rewards.
+6. If the robot still struggles at keeping the standing pose, you can also change the urdf definition using the (script)[https://github.com/kscalelabs/sim/blob/master/sim/scripts/create_fixed_torso.py] to fix the upper body or change joint limits.
+
+## Making it walk
+We set up the logic so that your new robot should start to walk with basic configuration after some modifications.
+1. To get things going it's best to start from the good standing pose, with knees slightly bent.
+See an example [here](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/resources/stompymini/joints.py#L113)
+2. The [gait reward](https://github.com/kscalelabs/sim/blob/aa97ddfddcaadcac64d4f83d986548cc7fc451a6/sim/envs/humanoids/stompy_config.py#L146) is the most crucial single reward. You have to adapt the hyperparameters to your robot design to get it to work.
+3. If the robots tends to jump, use only one limb you will have to adapt the overall rewards schema to enforce proper behavior.
+4. If the setup is correct, you should see positive results after first 200-300 epochs. However, the robust policy will requires 2000-3000 epochs.
+
+
+
+