### Install Robotic Transformer PyTorch

Source: https://github.com/lucidrains/robotic-transformer-pytorch/blob/main/README.md

Install the library using pip. This is the first step before using the RT1 model.

```bash
pip install robotic-transformer-pytorch
```

--------------------------------

### RT1 Training Example

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Demonstrates a typical training forward pass with video and text instructions. Ensure the model is initialized before this step. The output shape indicates batch, frames, number of actions, and action bins.

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

# Initialize model (example)
vit = MaxViT(
    num_classes=1000,
    dim_conv_stem=64,
    dim=96,
    dim_head=32,
    depth=(2, 2, 5, 2),
    window_size=7,
    mbconv_expansion_rate=4,
    mbconv_shrinkage_rate=0.25,
    dropout=0.1
)

model = RT1(
    vit=vit,
    num_actions=11,
    action_bins=256,
    depth=6,
    heads=8,
    dim_head=64,
    cond_drop_prob=0.2
)

video = torch.randn(2, 3, 6, 224, 224)  # (batch, channels, frames, height, width)
instructions = [
    'bring me that apple sitting on the table',
    'please pass the butter'
]

# Forward pass returns action logits
train_logits = model(video, instructions)
print(f"Training output shape: {train_logits.shape}")  # (2, 6, 11, 256)
# Output: (batch, frames, num_actions, action_bins)

# Convert logits to actions
actions = train_logits.argmax(dim=-1)  # (2, 6, 11)
print(f"Predicted actions shape: {actions.shape}")
```

--------------------------------

### Initialize and Use MaxViT Vision Backbone

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Configure the MaxViT vision backbone for classification or feature extraction. Ensure input images match the expected dimensions.

```python
import torch
from robotic_transformer_pytorch import MaxViT

# Initialize MaxViT vision backbone
vit = MaxViT(
    num_classes=1000,           # Number of output classes for classification
    dim_conv_stem=64,           # Dimension of convolutional stem
    dim=96,                     # Base dimension for transformer stages
    dim_head=32,                # Dimension per attention head
    depth=(2, 2, 5, 2),         # Number of transformer blocks per stage
    window_size=7,              # Window size for block/grid attention
    mbconv_expansion_rate=4,    # MBConv expansion rate
    mbconv_shrinkage_rate=0.25, # Squeeze-excitation shrinkage rate
    dropout=0.1,                # Dropout rate
    channels=3                  # Number of input channels
)

# Process a batch of images
images = torch.randn(2, 3, 224, 224)  # (batch, channels, height, width)

# Get classification output
class_logits = vit(images)
print(f"Classification output shape: {class_logits.shape}")  # (2, 1000)

# Get embeddings for downstream tasks (used by RT1)
embeddings = vit(images, return_embeddings=True)
print(f"Embedding output shape: {embeddings.shape}")  # (2, embed_dim, h, w)
```

--------------------------------

### Initialize MaxViT Backbone and RT1 Model

Source: https://github.com/lucidrains/robotic-transformer-pytorch/blob/main/README.md

Set up the MaxViT vision transformer backbone and the RT1 model. Ensure all required parameters are correctly configured for your task.

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

vit = MaxViT(
    num_classes = 1000,
    dim_conv_stem = 64,
    dim = 96,
    dim_head = 32,
    depth = (2, 2, 5, 2),
    window_size = 7,
    mbconv_expansion_rate = 4,
    mbconv_shrinkage_rate = 0.25,
    dropout = 0.1
)

model = RT1(
    vit = vit,
    num_actions = 11,
    depth = 6,
    heads = 8,
    dim_head = 64,
    cond_drop_prob = 0.2
)
```

--------------------------------

### RT1 Inference with Classifier-Free Guidance

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Performs inference using classifier-free guidance to enhance instruction following. Higher `cond_scale` values increase adherence to text instructions. The model should be in evaluation mode (`model.eval()`) and gradients disabled (`torch.no_grad()`).

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

# Initialize model (same as training)
vit = MaxViT(
    num_classes=1000,
    dim_conv_stem=64,
    dim=96,
    dim_head=32,
    depth=(2, 2, 5, 2),
    window_size=7,
    mbconv_expansion_rate=4,
    mbconv_shrinkage_rate=0.25,
    dropout=0.1
)

model = RT1(
    vit=vit,
    num_actions=11,
    action_bins=256,
    depth=6,
    heads=8,
    dim_head=64,
    cond_drop_prob=0.2
)

# Set to evaluation mode
model.eval()

# Video input
video = torch.randn(1, 3, 6, 224, 224)
instruction = ['pick up the red block and place it on the blue block']

# Inference with classifier-free guidance
with torch.no_grad():
    # cond_scale > 1.0 amplifies text conditioning
    eval_logits = model(video, instruction, cond_scale=3.0)

print(f"Inference output shape: {eval_logits.shape}")  # (1, 6, 11, 256)

# Decode actions from logits
predicted_actions = eval_logits.argmax(dim=-1)
print(f"Predicted actions: {predicted_actions.shape}")  # (1, 6, 11)

# Different guidance scales for comparison
for scale in [1.0, 2.0, 3.0, 5.0]:
    with torch.no_grad():
        logits = model(video, instruction, cond_scale=scale)
        actions = logits.argmax(dim=-1)
        print(f"Scale {scale}: action variance = {actions.float().var().item():.4f}")
```

--------------------------------

### Initialize RT1 Model

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Combine the vision backbone and transformer components to create the RT1 model. Requires a pre-configured MaxViT instance.

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

# Initialize the vision backbone
vit = MaxViT(
    num_classes=1000,
    dim_conv_stem=64,
    dim=96,
    dim_head=32,
    depth=(2, 2, 5, 2),
    window_size=7,
    mbconv_expansion_rate=4,
    mbconv_shrinkage_rate=0.25,
    dropout=0.1
)

# Initialize RT1 model
model = RT1(
    vit=vit,                            # MaxViT vision backbone
    num_actions=11,                     # Number of action dimensions
    action_bins=256,                    # Discretization bins per action
    depth=6,                            # Transformer depth
    heads=8,                            # Number of attention heads
    dim_head=64,                        # Dimension per head
    token_learner_ff_mult=2,            # TokenLearner feedforward multiplier
    token_learner_num_layers=2,         # TokenLearner layers
    token_learner_num_output_tokens=8,  # Tokens per frame
    cond_drop_prob=0.2,                 # Conditioning dropout for CFG training
    use_attn_conditioner=False          # Use attention-based text conditioning
)
```

--------------------------------

### RT1 Model Training and Evaluation

Source: https://github.com/lucidrains/robotic-transformer-pytorch/blob/main/README.md

Use the RT1 model for training by passing video and instruction data. For evaluation, enable eval mode and use conditional scaling for classifier-free guidance.

```python
video = torch.randn(2, 3, 6, 224, 224)

instructions = [
    'bring me that apple sitting on the table',
    'please pass the butter'
]

train_logits = model(video, instructions) # (2, 6, 11, 256) # (batch, frames, actions, bins) # after much training

model.eval()
eval_logits = model(video, instructions, cond_scale = 3.) # classifier free guidance with conditional scale of 3
```

--------------------------------

### Compress Visual Features with TokenLearner

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Use TokenLearner to aggregate spatial features into a fixed number of tokens. Supports both static images and video frame sequences.

```python
import torch
from robotic_transformer_pytorch import TokenLearner

# Initialize TokenLearner
token_learner = TokenLearner(
    dim=768,                 # Input feature dimension
    ff_mult=2,               # Feedforward multiplier for attention MLP
    num_output_tokens=8,     # Number of learned output tokens
    num_layers=2             # Number of layers in attention generation
)

# Input feature maps from vision backbone (batch, channels, height, width)
features = torch.randn(4, 768, 14, 14)

# Compress to learned tokens
learned_tokens = token_learner(features)
print(f"Output shape: {learned_tokens.shape}")  # (4, 768, 8)

# Works with video frames (batch, frames, channels, height, width)
video_features = torch.randn(2, 6, 768, 14, 14)
video_tokens = token_learner(video_features)
print(f"Video tokens shape: {video_tokens.shape}")  # (2, 6, 768, 8)
```

--------------------------------

### RT1 with Attention-based Text Conditioning

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Enables attention-based text conditioning by setting `use_attn_conditioner=True`. This method uses cross-attention for more expressive text-vision alignment compared to adaptive layer normalization. Additional arguments for the conditioner can be passed via `conditioner_kwargs`.

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

# Initialize vision backbone
vit = MaxViT(
    num_classes=1000,
    dim_conv_stem=64,
    dim=96,
    dim_head=32,
    depth=(2, 2, 5, 2),
    window_size=7,
    mbconv_expansion_rate=4,
    mbconv_shrinkage_rate=0.25,
    dropout=0.1
)

# Initialize RT1 with attention-based conditioning
model = RT1(
    vit=vit,
    num_actions=11,
    action_bins=256,
    depth=6,
    heads=8,
    dim_head=64,
    cond_drop_prob=0.2,
    use_attn_conditioner=True,  # Enable attention-based text conditioning
    conditioner_kwargs=dict(
        # Additional kwargs passed to AttentionTextConditioner
    )
)

# Training with attention conditioning
video = torch.randn(2, 3, 6, 224, 224)
instructions = [
    'carefully pick up the fragile glass',
    'stack the blocks in order of size'
]

train_logits = model(video, instructions)
print(f"Output shape: {train_logits.shape}")  # (2, 6, 11, 256)

# Inference with classifier-free guidance
model.eval()
with torch.no_grad():
    eval_logits = model(video, instructions, cond_scale=3.0)
```

--------------------------------

### RT1 with Pre-computed Text Embeddings

Source: https://context7.com/lucidrains/robotic-transformer-pytorch/llms.txt

Optimizes inference by pre-computing text embeddings, useful when the same instruction is used multiple times. Pass the computed `text_embeds` directly to the model instead of raw instructions.

```python
import torch
from robotic_transformer_pytorch import MaxViT, RT1

# Initialize model
vit = MaxViT(
    num_classes=1000,
    dim_conv_stem=64,
    dim=96,
    dim_head=32,
    depth=(2, 2, 5, 2),
    window_size=7,
    mbconv_expansion_rate=4,
    mbconv_shrinkage_rate=0.25,
    dropout=0.1
)

model = RT1(
    vit=vit,
    num_actions=11,
    action_bins=256,
    depth=6,
    heads=8,
    dim_head=64,
    cond_drop_prob=0.2
)

# Pre-compute text embeddings for a set of instructions
instructions = [
    'move the gripper to the left',
    'grasp the object firmly',
    'lift the object upward'
]

# Embed texts once
text_embeds = model.embed_texts(instructions)
print(f"Text embeddings shape: {text_embeds.shape}")

# Use pre-computed embeddings for multiple inference calls
model.eval()
video = torch.randn(3, 3, 6, 224, 224)

with torch.no_grad():
    # Pass text_embeds instead of texts
    logits = model(video, text_embeds=text_embeds, cond_scale=3.0)

print(f"Output shape: {logits.shape}")  # (3, 6, 11, 256)
```

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