diff --git a/README.md b/README.md
index da825a2..2afa7b3 100644
--- a/README.md
+++ b/README.md
@@ -34,17 +34,17 @@ This code contains implementation for teleoperation and imitation learning of Op
 ## Installation
 
 ```bash
-    conda create -n tv python=3.8
-    conda activate tv
-    pip install -r requirements.txt
-    cd act/detr && pip install -e .
+conda create -n tv python=3.8
+conda activate tv
+pip install -r requirements.txt
+cd act/detr && pip install -e .
 ```
 
 Install ZED sdk: https://www.stereolabs.com/developers/release/
 
 Install ZED Python API:
 ```
-    cd /usr/local/zed/ && python get_python_api.py
+cd /usr/local/zed/ && python get_python_api.py
 ```
 
 If you want to try teleoperation example in a simulated environment (teleop_hand.py):
@@ -61,35 +61,35 @@ For **Quest** local streaming, follow [this](https://github.com/OpenTeleVision/T
 2. check local ip address: 
 
 ```
-    ifconfig | grep inet
+ifconfig | grep inet
 ```
 Suppose the local ip address of the ubuntu machine is `192.168.8.102`.
 
 3. create certificate: 
 
 ```
-    mkcert -install && mkcert -cert-file cert.pem -key-file key.pem 192.168.8.102 localhost 127.0.0.1
+mkcert -install && mkcert -cert-file cert.pem -key-file key.pem 192.168.8.102 localhost 127.0.0.1
 ```
 ps. place the generated `cert.pem` and `key.pem` files in `teleop`.
 
 4. open firewall on server
 ```
-    sudo iptables -A INPUT -p tcp --dport 8012 -j ACCEPT
-    sudo iptables-save
-    sudo iptables -L
+sudo iptables -A INPUT -p tcp --dport 8012 -j ACCEPT
+sudo iptables-save
+sudo iptables -L
 ```
 or can be done with `ufw`:
 ```
-    sudo ufw allow 8012
+sudo ufw allow 8012
 ```
 5.
 ```
-    tv = OpenTeleVision(self.resolution_cropped, shm.name, image_queue, toggle_streaming, ngrok=False)
+tv = OpenTeleVision(self.resolution_cropped, shm.name, image_queue, toggle_streaming, ngrok=False)
 ```
 
 6. install ca-certificates on VisionPro
 ```
-    mkcert -CAROOT
+mkcert -CAROOT
 ```
 Copy the rootCA.pem via AirDrop to VisionPro and install it.
 
@@ -107,19 +107,19 @@ For Meta Quest3, installation of the certificate is not trivial. We need to use
 1. Install ngrok: https://ngrok.com/download
 2. Run ngrok
 ```
-    ngrok http 8012
+ngrok http 8012
 ```
 3. Copy the https address and open the browser on Meta Quest3 and go to the address.
 
 ps. When using ngrok for network streaming, remember to call `OpenTeleVision` with:
 ```
-    self.tv = OpenTeleVision(self.resolution_cropped, self.shm.name, image_queue, toggle_streaming, ngrok=True)
+self.tv = OpenTeleVision(self.resolution_cropped, self.shm.name, image_queue, toggle_streaming, ngrok=True)
 ```
 
 ### Simulation Teleoperation Example
 1. After setup up streaming with either local or network streaming following the above instructions, you can try teleoperating two robot hands in Issac Gym:
 ```
-    cd teleop && python teleop_hand.py
+cd teleop && python teleop_hand.py
 ```
 2. Go to your vuer site on VisionPro, click `Enter VR` and ``Allow`` to enter immersive environment.
 
@@ -137,18 +137,18 @@ ps. When using ngrok for network streaming, remember to call `OpenTeleVision` wi
 
 5. To train ACT, run:
 ```
-    python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt
+python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt
 ```
 
 6. After training, save jit for the desired checkpoint:
 ```
-    python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt\
+python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt\
                                --save_jit --resume_ckpt 25000
 ```
 
 7. You can visualize the trained policy with inputs from dataset using ``scripts/deploy_sim.py``, example usage:
 ```
-    python deploy_sim.py --taskid 00 --exptid 01 --resume_ckpt 25000
+python deploy_sim.py --taskid 00 --exptid 01 --resume_ckpt 25000
 ```
 
 ## Citation