---
license: mit
library_name: stable-baselines3
tags:
  - reinforcement-learning
  - robotics
  - autonomous-navigation
  - ros2
  - gazebo
  - sac
  - lidar
  - camera
  - multi-input
pipeline_tag: reinforcement-learning
---

# RC Car Autonomous Navigation — SAC (Camera + LiDAR)

A **Soft Actor-Critic (SAC)** agent trained to autonomously navigate an RC car in a simulated Gazebo environment using both **camera images** and **LiDAR sensor data** as observations. The agent learns to reach target positions while avoiding obstacles.

---

## Model Description

This model uses a **MultiInputPolicy** with a hybrid perception backbone:

- **Visual stream** — RGB camera frames processed by a CNN (NatureCNN)
- **Sensor stream** — LiDAR point cloud + navigation state processed by an MLP

Both streams are fused and fed into the SAC actor/critic networks for end-to-end policy learning.

| Property | Value |
|---|---|
| Algorithm | Soft Actor-Critic (SAC) |
| Policy | `MultiInputPolicy` |
| Observation | `Dict` — image `(64×64×3)` + sensor vector `(184,)` |
| Action Space | `Box([-1, -1], [1, 1])` — speed & steering |
| Simulator | Gazebo (Ignition/Harmonic) via ROS 2 |
| Framework | Stable-Baselines3 |

---

## Environments

Two training environments are available:

### `RcCarTargetEnv`
The robot spawns at a random position and must navigate to a randomly placed target (red sphere marker). No dynamic obstacles.

### `RcCarComplexEnv`
Same goal-reaching task but with **6 randomly placed box obstacles** that are reshuffled every episode, requiring active collision avoidance.

---

## Observation Space

```python
spaces.Dict({
    "image": spaces.Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8),
    "sensor": spaces.Box(low=0.0, high=1.0, shape=(184,), dtype=np.float32)
})
```

The `sensor` vector contains:
- **[0:180]** — Normalised LiDAR ranges (180 beams, max range 10 m)
- **[180]** — Normalised linear speed
- **[181]** — Normalised steering angle
- **[182]** — Normalised distance to target (clipped at 10 m)
- **[183]** — Normalised relative angle to target

---

## Action Space

```python
spaces.Box(low=[-1.0, -1.0], high=[1.0, 1.0], dtype=np.float32)
```

| Index | Meaning | Scale |
|---|---|---|
| `action[0]` | Linear speed | × 1.0 m/s |
| `action[1]` | Steering angle | × 0.6 rad/s |

Steering is smoothed with a low-pass filter: `steer = 0.6 × prev + 0.4 × target`.

---

## Reward Function

### `RcCarTargetEnv`
| Event | Reward |
|---|---|
| Progress toward target | `Δdistance × 40.0` |
| Reached target (< 0.6 m) | `+100.0` |
| Collision (LiDAR < 0.22 m) | `−50.0` |
| Per-step penalty | `−0.05` |

### `RcCarComplexEnv`
| Event | Reward |
|---|---|
| Progress toward target | `Δdistance × 40.0` |
| Forward speed bonus (on progress) | `+speed × 0.5` |
| Proximity warning (LiDAR < 0.5 m) | `−0.5` |
| Collision | `−50.0` |
| Reached target | `+100.0` |
| Per-step penalty | `−0.1` |

---

## Training Setup

```python
model = SAC(
    "MultiInputPolicy",
    env,
    learning_rate=3e-4,
    buffer_size=50000,
    policy_kwargs=dict(
        net_arch=dict(pi=[256, 256], qf=[256, 256])
    ),
    device="auto"
)
```

- **Action repeat:** 4 steps per agent decision
- **Frame stacking:** configurable via Hydra config (`n_stack`)
- **Vectorised env:** `DummyVecEnv` + `VecFrameStack` (channels_order=`"last"`)
- **Experiment tracking:** Weights & Biases (W&B) with SB3 callback

---

## Hardware & Software Requirements

| Component | Requirement |
|---|---|
| ROS 2 | Humble or newer |
| Gazebo | Ignition Fortress / Harmonic |
| Python | 3.10+ |
| PyTorch | 2.0+ |
| stable-baselines3 | ≥ 2.0 |
| gymnasium | ≥ 0.29 |
| opencv-python | any recent |
| cv_bridge | ROS 2 package |

---

## How to Use

### 1. Install dependencies
```bash
pip install stable-baselines3 wandb hydra-core gymnasium opencv-python
```

### 2. Launch the simulator
```bash
ros2 launch my_bot_pkg sim.launch.py
```

### 3. Run training
```bash
python train.py experiment.mode=target experiment.total_timesteps=500000
```

### 4. Load and run inference
```python
from stable_baselines3 import SAC
from rc_car_envs_camera import RcCarTargetEnv

env = RcCarTargetEnv()
model = SAC.load("sac_target_camera_final", env=env)

obs, _ = env.reset()
while True:
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, _ = env.reset()
```

---

## Project Structure

```
├── rc_car_envs_camera.py   # Gym environments (Base, Target, Complex)
├── train.py                # Hydra-based training entry point
├── configs/
│   └── config.yaml         # Hydra config (mode, timesteps, wandb, etc.)
└── models/                 # Saved checkpoints (W&B)
```

---

## Limitations & Known Issues

- Training requires a live ROS 2 + Gazebo session; no offline/headless mode currently.
- `DummyVecEnv` runs a single environment — parallelisation would require `SubprocVecEnv` with careful ROS node naming.
- Camera latency under heavy load may cause the `scan_received` / `cam_received` wait loop to time out, potentially delivering stale observations.
- The collision threshold (0.22 m) is tuned for the specific robot mesh; adjust for different URDF geometries.

---

## Citation

If you use this environment or training code in your research, please cite:

```bibtex
@misc{rccar_sac_nav,
  title  = {RC Car Autonomous Navigation with SAC (Camera + LiDAR)},
  year   = {2025},
  url    = {https://huggingface.co/Hajorda/SAC_Complex_Camera}
}
```

---

## License

MIT License