Duplicate from lerobot/pi0_base

Co-authored-by: Pepijn Kooijmans <pepijn223@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+language:
+- en
+library_name: lerobot
+pipeline_tag: robotics
+tags:
+- vision-language-action
+- imitation-learning
+- lerobot
+inference: false
+license: gemma
+---
+
+# π₀ (Pi0) (LeRobot)
+
+π₀ is a Vision-Language-Action (VLA) foundation model from Physical Intelligence that jointly reasons over vision, language, and actions to control robots, serving as the base architecture that later enabled π₀.₅’s open-world generalization.
+
+
+**Original paper:** π0: A Vision-Language-Action Flow Model for General Robot Controlion  
+**Reference implementation:** https://github.com/Physical-Intelligence/openpi  
+**LeRobot implementation:** Follows the original reference code for compatibility.
+
+
+## Model description
+
+- **Inputs:** images (multi-view), proprio/state, optional language instruction
+- **Outputs:** continuous actions
+- **Training objective:** flow matching
+- **Action representation:** continuous
+- **Intended use:** Base model to fine tune on your specific use case
+
+
+## Quick start (inference on a real batch)
+
+### Installation
+
+```bash
+pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"
+```
+For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation
+
+### Load model + dataset, run `select_action`
+
+```python
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_pre_post_processors
+
+# Swap this import per-policy
+from lerobot.policies.pi0 import PI0Policy
+
+# load a policy
+model_id = "lerobot/pi0_base"  # <- swap checkpoint
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+policy = PI0Policy.from_pretrained(model_id).to(device).eval()
+
+preprocess, postprocess = make_pre_post_processors(
+    policy.config,
+    model_id,
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
+# load a lerobotdataset
+dataset = LeRobotDataset("lerobot/libero")
+
+# pick an episode
+episode_index = 0
+
+# each episode corresponds to a contiguous range of frame indices
+from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
+to_idx   = dataset.meta.episodes["dataset_to_index"][episode_index]
+
+# get a single frame from that episode (e.g. the first frame)
+frame_index = from_idx
+frame = dict(dataset[frame_index])
+
+batch = preprocess(sample)
+with torch.inference_mode():
+    pred_action = policy.select_action(frame)
+    # use your policy postprocess, this post process the action
+    # for instance unnormalize the actions, detokenize it etc..
+    pred_action = postprocess(pred_action)
+```
+
+
+## Training step (loss + backward)
+
+If you’re training / fine-tuning, you typically call `forward(...)` to get a loss and then:
+
+```python
+policy.train()
+batch = dict(dataset[0])
+batch = preprocess(batch)
+
+loss, outputs = policy.forward(batch)
+loss.backward()
+
+```
+
+> Notes:
+> 
+> - Some policies expose `policy(**batch)` or return a dict; keep this snippet aligned with the policy API.
+> - Use your trainer script (`lerobot-train`) for full training loops.
+
+
+## How to train / fine-tune
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --output_dir=./outputs/[RUN_NAME] \
+  --job_name=[RUN_NAME] \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id> \
+  --policy.path=lerobot/[BASE_CHECKPOINT] \
+  --policy.dtype=bfloat16 \
+  --policy.device=cuda \
+  --steps=100000 \
+  --batch_size=4
+```
+
+Add policy-specific flags below:
+
+- `-policy.chunk_size=...`
+- `-policy.n_action_steps=...`
+- `-policy.max_action_tokens=...`
+- `-policy.gradient_checkpointing=true`
+
+
+## Real-World Inference & Evaluation
+
+You can use the `record` script from [**`lerobot-record`**](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_record.py) with a policy checkpoint as input, to run inference and evaluate your policy. 
+
+For instance, run this command or API example to run inference and record 10 evaluation episodes:
+
+```
+lerobot-record  \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --robot.id=my_awesome_follower_arm \
+  --display_data=false \
+  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  # <- Teleop optional if you want to teleoperate in between episodes \
+  # --teleop.type=so100_leader \
+  # --teleop.port=/dev/ttyACM0 \
+  # --teleop.id=my_awesome_leader_arm \
+  --policy.path=${HF_USER}/my_policy
+```

config.json

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+{
+    "type": "pi0",
+    "n_obs_steps": 1,
+    "input_features": {
+        "observation.images.base_0_rgb": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        },
+        "observation.images.left_wrist_0_rgb": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        },
+        "observation.images.right_wrist_0_rgb": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        },
+        "observation.state": {
+            "type": "STATE",
+            "shape": [
+                32
+            ]
+        }
+    },
+    "output_features": {
+        "action": {
+            "type": "ACTION",
+            "shape": [
+                32
+            ]
+        }
+    },
+    "device": "cuda",
+    "use_amp": false,
+    "push_to_hub": true,
+    "repo_id": null,
+    "private": null,
+    "tags": null,
+    "license": null,
+    "paligemma_variant": "gemma_2b",
+    "action_expert_variant": "gemma_300m",
+    "dtype": "float32",
+    "chunk_size": 50,
+    "n_action_steps": 50,
+    "max_action_dim": 32,
+    "max_state_dim": 32,
+    "num_inference_steps": 10,
+    "time_sampling_beta_alpha": 1.5,
+    "time_sampling_beta_beta": 1.0,
+    "min_period": 0.004,
+    "max_period": 4.0,
+    "image_resolution": [
+        224,
+        224
+    ],
+    "gradient_checkpointing": false,
+    "compile_model": false,
+    "compile_mode": "max-autotune",
+    "optimizer_lr": 2.5e-05,
+    "optimizer_betas": [
+        0.9,
+        0.95
+    ],
+    "optimizer_eps": 1e-08,
+    "optimizer_weight_decay": 0.01,
+    "optimizer_grad_clip_norm": 1.0,
+    "scheduler_warmup_steps": 1000,
+    "scheduler_decay_steps": 30000,
+    "scheduler_decay_lr": 2.5e-06,
+    "tokenizer_max_length": 48
+}

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+oid sha256:8229fd9a7c3c2aafc1e223567b61b5fe3e25eef873bb4233928dbee4bd836303
+size 14005618584

policy_postprocessor.json

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+{
+  "name": "policy_postprocessor",
+  "steps": [
+    {
+      "registry_name": "unnormalizer_processor",
+      "config": {
+        "eps": 1e-08,
+        "features": {},
+        "norm_map": {
+          "VISUAL": "IDENTITY",
+          "STATE": "MEAN_STD",
+          "ACTION": "MEAN_STD"
+        }
+      }
+    },
+    {
+      "registry_name": "absolute_actions_processor",
+      "config": {
+        "enabled": false
+      }
+    },
+    {
+      "registry_name": "device_processor",
+      "config": {
+        "device": "cpu",
+        "float_dtype": null
+      }
+    }
+  ]
+}

policy_preprocessor.json

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+{
+  "name": "policy_preprocessor",
+  "steps": [
+    {
+      "registry_name": "rename_observations_processor",
+      "config": {
+        "rename_map": {}
+      }
+    },
+    {
+      "registry_name": "to_batch_processor",
+      "config": {}
+    },
+    {
+      "registry_name": "pi0_new_line_processor",
+      "config": {}
+    },
+    {
+      "registry_name": "tokenizer_processor",
+      "config": {
+        "max_length": 48,
+        "task_key": "task",
+        "padding_side": "right",
+        "padding": "max_length",
+        "truncation": true,
+        "tokenizer_name": "google/paligemma-3b-pt-224"
+      }
+    },
+    {
+      "registry_name": "device_processor",
+      "config": {
+        "device": "cuda",
+        "float_dtype": null
+      }
+    },
+    {
+      "registry_name": "relative_actions_processor",
+      "config": {
+        "enabled": false,
+        "exclude_joints": [
+          "gripper"
+        ],
+        "action_names": null
+      }
+    },
+    {
+      "registry_name": "normalizer_processor",
+      "config": {
+        "eps": 1e-08,
+        "features": {},
+        "norm_map": {
+          "VISUAL": "IDENTITY",
+          "STATE": "MEAN_STD",
+          "ACTION": "MEAN_STD"
+        }
+      }
+    }
+  ]
+}