Add API Examples (#2289)

* (unscrewing things up) (#2288)

* fix: expose a function explicitly building a frame for inference

* fix: first make dataset frame, then make ready for inference

* fix: reducing reliance on lerobot record for policy's ouptuts too

* fix: encapsulating squeezing out + device handling from predict action

* fix: remove duplicated call to build_inference_frame and add a function to only perform data type handling (whole conversion is: keys matching + data type conversion)

* refactor(envs): add custom-observation-size (#2167)

* fix: add MockMotorBus to MockRobot

* rl: first drafts

* add: all components of HIL SERL

* fix: actor block works

* fix: less friction, less friction

* add: hil-serl complete example

* fix: dataset names

* fix: restructuring example folder

* fix: act works but found bug in how ACT works

* fix: same path for both pre and postprocessors

* fix: paths

* add: example usage for act

* add: using ACT example

* fix: training examples

* fix: using examples

* fix: camera index

* fix: rename workflows into tutorial so that the path of the files is lerobot/examples/tutorial/...

* fix: upload everything in one repo

* fix: model name

* fix: simplify model path

* add: VLAs example

---------

Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com>

* fix: minor fix using named attributes

* fix: change model to act

* fix: named attributes for inference frame building

* fix: minor fixes to smolvla

* fix: small changes to pi0

* remove: old file that should have never been committed (ups sorry sorry)

---------

Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com>

examples/tutorial/rl/reward_classifier_example.py

@@ -0,0 +1,62 @@
+import torch
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
+
+# Device to use for training
+device = "mps"  # or "cuda", or "cpu"
+
+# Load the dataset used for training
+repo_id = "lerobot/example_hil_serl_dataset"
+dataset = LeRobotDataset(repo_id)
+
+# Configure the policy to extract features from the image frames
+camera_keys = dataset.meta.camera_keys
+
+config = RewardClassifierConfig(
+    num_cameras=len(camera_keys),
+    device=device,
+    # backbone model to extract features from the image frames
+    model_name="microsoft/resnet-18",
+)
+
+# Make policy, preprocessor, and optimizer
+policy = make_policy(config, ds_meta=dataset.meta)
+optimizer = config.get_optimizer_preset().build(policy.parameters())
+preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
+
+
+classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+
+# Instantiate a dataloader
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
+
+# Training loop
+num_epochs = 5
+for epoch in range(num_epochs):
+    total_loss = 0
+    total_accuracy = 0
+    for batch in dataloader:
+        # Preprocess the batch and move it to the correct device.
+        batch = preprocessor(batch)
+
+        # Forward pass
+        loss, output_dict = policy.forward(batch)
+
+        # Backward pass and optimization
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item()
+        total_accuracy += output_dict["accuracy"]
+
+    avg_loss = total_loss / len(dataloader)
+    avg_accuracy = total_accuracy / len(dataloader)
+    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
+
+print("Training finished!")
+
+# You can now save the trained policy.
+policy.push_to_hub(classifier_id)