Update readme & remove example 1 (#108)

Co-authored-by: Remi <re.cadene@gmail.com>
- Update instructions for installing the library
- Remove deprecated example 1 (as we are now only using `LeRobotDataset` since #91)

examples/2_evaluate_pretrained_policy.py

@@ -0,0 +1,38 @@
+"""
+This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
+training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
+"""
+
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+
+from lerobot.common.utils.utils import init_hydra_config
+from lerobot.scripts.eval import eval
+
+# Get a pretrained policy from the hub.
+# TODO(alexander-soare): This no longer works until we upload a new model that uses the current configs.
+hub_id = "lerobot/diffusion_policy_pusht_image"
+folder = Path(snapshot_download(hub_id))
+# OR uncomment the following to evaluate a policy from the local outputs/train folder.
+# folder = Path("outputs/train/example_pusht_diffusion")
+
+config_path = folder / "config.yaml"
+weights_path = folder / "model.pt"
+
+# Override some config parameters to do with evaluation.
+overrides = [
+    f"policy.pretrained_model_path={weights_path}",
+    "eval_episodes=10",
+    "rollout_batch_size=10",
+    "device=cuda",
+]
+
+# Create a Hydra config.
+cfg = init_hydra_config(config_path, overrides)
+
+# Evaluate the policy and save the outputs including metrics and videos.
+eval(
+    cfg,
+    out_dir=f"outputs/eval/example_{cfg.env.name}_{cfg.policy.name}",
+)