merge: <- master

.gitmodules

@@ -0,0 +1,6 @@
+[submodule "ros/newton_ros"]
+	path = ros/pkgs/newton_ros
+	url = https://github.com/TheNewtonCapstone/newton-ros
+[submodule "ros/newton_sim_ros"]
+	path = ros/pkgs/newton_sim_ros
+	url = https://github.com/TheNewtonCapstone/newton-sim-ros

README.md

@@ -1,50 +1,52 @@
-# TWIP - Isaac Sim
-This repo contains the code and configuration used during our journey of developing TWIP using NVIDIA's Isaac Sim and rl games.
+# Newton - Isaac Sim
+This repo contains the code and configuration used during our journey of developing Newton using NVIDIA's Isaac Sim.
 
 ## Requirements
 - Ubuntu 22.04 LTS (Works with Pop OS as well)
- - Nvidia GPU with 510.73.05+ drivers (`nvidia-smi` to make sure these are set up)
-- Isaac Sim (tested with version `4.0.0`)
-- RL games
+- Nvidia GPU (RTX 2070 & above) with 510.73.05+ drivers (execute `nvidia-smi` in your terminal to make sure the drivers are set up)
+- Isaac Sim (tested with version `4.2.0`)
+- Stable Baselines 3
 - Anaconda | Miniconda
+  - If using ROS: install Mamba (optional; with `conda install mamba -c conda-forge`) for faster package installation
 
 ## Isaac Sim Setup
-- Download Isaac Sim by following the steps found within Nvidia's installation (guide)[https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_workstation.html].
+- Download Isaac Sim by following the steps found within Nvidia's installation [guide](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_workstation.html).
 - Run Isaac sim from the Omniverse application to make sure it runs properly
-- Clone this repository `git clone https://github.com/TheNewtonCapstone/twip-isaac-sim`
-- Remove any folder named `_isaac_sim`
-- Run `cd twip-isaac-sim && ln -s home/YOUR_NAME/.local/share/ov/pkg/isaac-sim-4.0.0`
-- Create the conda environment `conda env create -f environment.yml` 
+- Clone this repository `git clone https://github.com/TheNewtonCapstone/newton-isaac-sim`
+- Run `cd newton-isaac-sim && ln -s /home/YOUR_USERNAME/.local/share/ov/pkg/isaac-sim-4.2.0 _isaac_sim`
+- Create the conda environment `conda env create -f environment.yml`
+  - If using ROS: `conda env create -f environment_ros.yml`
 
 ## Repo Structure
- - `twip.py`: Main script used for training/testing/exporting models
-- `export.py`: Script for exporting models as ONNX files
-- `cfg/task/`: YAML configs for each task defining environment parameters and domain randomization
-- `cfg/train/`: YAML configs for each task defining RL model and hyperparameters
-- `tasks/`: Class definitions and assets for each task
-- `runs/`: Model checkpoints and summaries will be saved here
-
-## Usage
+- `newton.py`: Main script used for training/testing/exporting models
+- `environment(_ros).yml`: Project dependency requirements
+- `core/`: Contains all the core functionalities (Simulation, Training, Animation...)
+- `assets/`: Contains miscellaneous assets (e.g. animations, USD files)
+- `docs/`: Contains general project documentation 
+- `scripts/`: Contains helper scripts such as the animation keyframe extractor
+- `configs/`: Contains the configuration files
+- `runs/`: Model checkpoints and summaries will be saved here (by default)
+- `apps/`: Contains the Isaac Sim applications configuration files
 
 ### Running Isaac Sim
-The entry point of our project is `twip.py`. To run our project you must configure the environment:
+The entry point of our project is `newton.py`. **Before running your IDE** (and in any terminal that you wish to run the project in), you must configure the environment:
 - `conda activate isaac-sim`
-- `source _isaac_sim/setup_conda_env.sh`
-- `python twip.py --sim-only --num-envs 4`
+- `source setup.sh`
+  - If using ROS: this script will automatically source the Newton ROS workspace
+
+Now with your environment configured, within the same terminal, you can open your desired IDE:
+- `pycharm` for Pycharm (recommended)
+- `code` for VS Code
+
+We have provided a simple CLI to allow you to start training your very own Newton. All you have to do is run `newton.py`!
+Here is how the first option should look like: 
 
-`--sim-only` is used to run the simluation alone without any reinforcement learning. `--num-envs` specifies the number of twip environments within the simulation. To see all the options, run `twip.py --help`.
+![docs/assets/mode_select.png](docs/assets/mode_select.png)
 
 ### Training
-- `python train.py task={task_name}` to begin training
+- execute `python newton.py` and select `training`.
 - Models are saved as `runs/{TaskName}/nn/{checkpoint_name}.pth`
 
 ### Exporting ONNX
- - `python twip.py --checkpoint="runs/{checkpoint_name}/nn/{task_name}.pth --export-onnx"` to export
- - Model is exported to `runs/{checkpoint_name}/nn/{task_name}.pth.onnx`
-
-### Adding a New Task (?)
-Create a new folder called `tasks/{task_name}`. The following files will be needed:
-- `tasks/{task_name}/{task_name}.py`: Class defining the task. Rewards, observations, actions are all defined here.
-- `tasks/{task_name}/assets/`: URDF and meshes should be placed here.
-- `cfg/task/{TaskName}.yaml`: Config file defining environment parameters and domain randomization
-- `cfg/train/{TaskName}PPO.yaml`: Config file defining hyperparameters for training
+- execute`python newton.py` and select `Exporting`.
+- Model is exported to `runs/{checkpoint_name}/nn/{task_name}.pth.onnx`

configs/robots/newton.yaml

@@ -3,31 +3,73 @@ robot_type: quadruped
 
 # Kinematics
 joints:
-  limits:
-    positions:
-      FR_HAA: [-45, 45]
+  gear_ratios:
+    FL_HAA: 9
+    FR_HAA: 9
+    HL_HAA: 9
+    HR_HAA: 9
+    FL_HFE: 9
+    FR_HFE: 9
+    HL_HFE: 9
+    HR_HFE: 9
+    FL_KFE: 9
+    FR_KFE: 9
+    HL_KFE: 9
+    HR_KFE: 9
+
+  effort_saturation: # of the input (i.e. the motor; before applying any gear ratio)
+    FL_HAA: 0.4
+    FR_HAA: 0.4
+    HL_HAA: 0.4
+    HR_HAA: 0.4
+    FL_HFE: 0.4
+    FR_HFE: 0.4
+    HL_HFE: 0.4
+    HR_HFE: 0.4
+    FL_KFE: 0.4
+    FR_KFE: 0.4
+    HL_KFE: 0.4
+    HR_KFE: 0.4
+
+  limits: # of the output (i.e. the joint itself, after applying any gear ratio)
+    positions: # deg
       FL_HAA: [-45, 45]
-      HR_HAA: [-45, 45]
+      FR_HAA: [-45, 45]
       HL_HAA: [-45, 45]
-      FR_HFE: [-90, 90]
-      HR_HFE: [-90, 90]
+      HR_HAA: [-45, 45]
       FL_HFE: [-90, 90]
+      FR_HFE: [-90, 90]
       HL_HFE: [-90, 90]
-      FR_KFE: [-175, 175]
+      HR_HFE: [-90, 90]
       FL_KFE: [-175, 175]
-      HR_KFE: [-175, 175]
+      FR_KFE: [-175, 175]
       HL_KFE: [-175, 175]
+      HR_KFE: [-175, 175]
+
+    velocities: # deg/s
+      FL_HAA: 5440
+      FR_HAA: 5440
+      HL_HAA: 5440
+      HR_HAA: 5440
+      FL_HFE: 5440
+      FR_HFE: 5440
+      HL_HFE: 5440
+      HR_HFE: 5440
+      FL_KFE: 5440
+      FR_KFE: 5440
+      HL_KFE: 5440
+      HR_KFE: 5440
 
-    velocities:
-      FR_HAA: 8000
-      FL_HAA: 8000
-      HR_HAA: 8000
-      HL_HAA: 8000
-      FR_HFE: 8000
-      HR_HFE: 8000
-      FL_HFE: 8000
-      HL_HFE: 8000
-      FR_KFE: 8000
-      FL_KFE: 8000
-      HR_KFE: 8000
-      HL_KFE: 8000
+    efforts: # Nm
+      FL_HAA: 6.3
+      FR_HAA: 6.3
+      HL_HAA: 6.3
+      HR_HAA: 6.3
+      FL_HFE: 6.3
+      FR_HFE: 6.3
+      HL_HFE: 6.3
+      HR_HFE: 6.3
+      FL_KFE: 6.3
+      FR_KFE: 6.3
+      HL_KFE: 6.3
+      HR_KFE: 6.3

configs/ros.yaml

@@ -0,0 +1,42 @@
+enabled: true
+namespace: sim
+domain_id: 25
+
+defaults:
+  qos:
+    history: 0 # 0: system default, 1: keep last, 2: keep all, 3: unknown
+    depth: 5 # number of samples in history
+    reliability: 0 # 0: system default, 1: reliable, 2: best effort, 3: unknown
+    durability: 0 # 0: system default, 1: transient_local, 2: volatile, 3: unknown
+    lifespan:
+      seconds: 0
+      nanoseconds: 0
+    deadline:
+      seconds: 0
+      nanoseconds: 0
+    liveliness: 0 # 0: system default, 1: automatic, 2: manual_by_topic, 3: unknown
+    liveliness_lease_duration:
+      seconds: 0
+      nanoseconds: 0
+
+nodes:
+  imu:
+    name: imu_node
+    pub_sim_topic: /sim/imu
+    pub_real_topic: /imu
+    pub_period: 5
+    pub_qos: None
+
+  contact:
+    name: contact_node
+    pub_sim_topic: /sim/contact
+    pub_real_topic: /contact
+    pub_period: 10
+    pub_qos: None
+
+  joints_controller:
+    name: joints_controller_node
+    pub_sim_topic: /sim/joints
+    pub_real_topic: /joints
+    pub_period: 5
+    pub_qos: None

configs/tasks/newton_idle_task.yaml

@@ -9,6 +9,7 @@ base_lr: 3e-6
 episode_length: 500
 
 newton:
+# TODO: Remove inverse_control_frequency as a task parameter
   inverse_control_frequency: 4  # we're aiming for a total compute budget of 2 times the physics timestep (0.005s * 4 = 0.02s)
 
   # TODO: Add gains as a configuration parameter

configs/world.yaml

@@ -1,6 +1,9 @@
 physics_dt: 0.005000
-stage_units_in_meters: 1.0
+# TODO: Fully implement control_dt functionality across all systems
+#   The idea is to have everything inside of the Universe object, so it's more easily accessed from one place
+control_dt: 0.020000
 rendering_dt: 0.01667
+stage_units_in_meters: 1.0
 backend: torch
 device: cuda
 

core/actuators/__init__.py

@@ -0,0 +1,2 @@
+from .base_actuator import BaseActuator
+from .dc_actuator import DCActuator