# ⏱️ Explicit Estimator

In [teacher_student framework](teacher_student.md), the latent vector output by the teacher encoder is an encoded information of privileged information $x_t$. The latent vector is some kind of representation of the privileged information, without explicit physical meaning. However, according to our experience in [Deploy to Real Robot](https://genesis-lr.readthedocs.io/en/latest/user_guide/getting_started/deploy_to_real_robot.html), the existence of `base_lin_vel` can improve the performance of the agent significantly. Then the problem is how can we obtain `base_lin_vel` in the real robot. 

From the perspective of model-based control, Kalman Filter can help us estimate `base_lin_vel` using feedback of robot states. However, this kind of method usually relies on some assumption, limiting their versatility. With the great power of neural network, how can we handle it? [EstimatorNet](https://arxiv.org/abs/2202.05481) was proposed to solve this. 

## Framework Analysis

Basically, EstimatorNet has the similar form of diagram with [teacher-student framework](teacher_student.md). The biggest difference is that **the encoder output in EstimatorNet is trained to approximate explicit physical values, such as base linear velocity, foot contact probability, foot height and so on**. The diagram of EstimatorNet is shown as below, where $e_t$ is true value of explicit vector, $o_t^H=[o_t, o_{t-1},...,o_{t-H+1}]$ is observation history with length of H steps, $\hat{e}_t$ is predicted explicit vector from estimator network, $a_t$ is the action, $s_t$ is the state of the environment, $\hat{V}_t$ is the estimated value from the critic.

```{figure} ../../_static/images/estimatornet_diagram.png
```

## Implementation

The implementation of this method is similar with [teacher_student framework](teacher_student.md). Readers can look through files with suffix of `ee` to find the implementation.

To train a explicit estimator policy, type the following command:
```bash
python train.py --task=go2_ee --headless
```

To play it, type the following command:
```bash
python play.py --task=go2_ee --load_run=session_name
```

## Demonstration

We have provided reference deployment code of explicit estimator policy in [go2_deploy_python](https://github.com/lupinjia/go2_deploy_python) and [tron1_rl_deploy_python](https://github.com/lupinjia/tron1-rl-deploy-python).

Unitree Go2:

```{figure} ../../_static/images/ee_demo.gif
```

TRON1_PF:

```{figure} ../../_static/images/tron1_pf_ee_demo.gif
```


## References
1. [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://arxiv.org/abs/2202.05481)