Duplicate from lerobot/xvla-folding

Co-authored-by: Jade Choghari <jadechoghari@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: apache-2.0
+---
+
+# X-VLA (LeRobot)
+
+X-VLA is a Vision-Language-Action foundation model that uses soft prompts to handle cross-embodiment and cross-domain robot control within a unified Transformer architecture.
+
+A fine-tuned dexterous manipulation model trained on the high-quality Soft-FOLD cloth folding dataset. Achieves 100% success rate over 2 hours of continuous cloth folding..
+
+**Original paper:** [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://arxiv.org/abs/2510.10274)
+**Reference implementation:** https://github.com/2toinf/X-VLA  
+**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[xvla]"
+```
+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.xvla.modeling_xvla import XVLAPolicy
+
+# load a policy
+model_id = "lerobot/xvla-folding"  # <- swap checkpoint
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+policy = XVLAPolicy.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 (we will replace with a simpler dataset)
+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(frame)
+with torch.inference_mode():
+    pred_action = policy.select_action(batch)
+    # 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": "xvla",
+    "n_obs_steps": 1,
+    "input_features": {
+        "observation.images.image": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                256,
+                256
+            ]
+        },
+        "observation.images.image2": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                256,
+                256
+            ]
+        },
+        "observation.state": {
+            "type": "STATE",
+            "shape": [
+                8
+            ]
+        },
+        "observation.images.empty_camera_0": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        }
+    },
+    "output_features": {
+        "action": {
+            "type": "ACTION",
+            "shape": [
+                20
+            ]
+        }
+    },
+    "device": "cuda",
+    "use_amp": false,
+    "push_to_hub": true,
+    "repo_id": null,
+    "private": null,
+    "tags": null,
+    "license": null,
+    "pretrained_path": null,
+    "chunk_size": 30,
+    "n_action_steps": 30,
+    "normalization_mapping": {
+        "STATE": "IDENTITY",
+        "ACTION": "MEAN_STD",
+        "VISUAL": "IDENTITY"
+    },
+    "florence_config": {
+        "model_type": "florence2",
+        "bos_token_id": 0,
+        "eos_token_id": 2,
+        "ignore_index": -100,
+        "pad_token_id": 1,
+        "projection_dim": 1024,
+        "text_config": {
+            "vocab_size": 51289,
+            "activation_dropout": 0.1,
+            "activation_function": "gelu",
+            "attention_dropout": 0.1,
+            "d_model": 1024,
+            "decoder_attention_heads": 16,
+            "decoder_layers": 12,
+            "encoder_attention_heads": 16,
+            "encoder_layers": 12,
+            "dropout": 0.1,
+            "max_position_embeddings": 4096,
+            "num_hidden_layers": 12,
+            "num_beams": 3
+        },
+        "vision_config": {
+            "model_type": "davit",
+            "drop_path_rate": 0.1,
+            "patch_size": [
+                7,
+                3,
+                3,
+                3
+            ],
+            "patch_stride": [
+                4,
+                2,
+                2,
+                2
+            ],
+            "patch_padding": [
+                3,
+                1,
+                1,
+                1
+            ],
+            "patch_prenorm": [
+                false,
+                true,
+                true,
+                true
+            ],
+            "enable_checkpoint": false,
+            "dim_embed": [
+                256,
+                512,
+                1024,
+                2048
+            ],
+            "num_heads": [
+                8,
+                16,
+                32,
+                64
+            ],
+            "num_groups": [
+                8,
+                16,
+                32,
+                64
+            ],
+            "depths": [
+                1,
+                1,
+                9,
+                1
+            ],
+            "window_size": 12,
+            "projection_dim": 1024,
+            "visual_temporal_embedding": {
+                "type": "COSINE",
+                "max_temporal_embeddings": 100
+            },
+            "image_pos_embed": {
+                "type": "learned_abs_2d",
+                "max_pos_embeddings": 50
+            },
+            "image_feature_source": [
+                "spatial_avg_pool",
+                "temporal_avg_pool"
+            ]
+        },
+        "vocab_size": 51289,
+        "torch_dtype": "float16",
+        "is_encoder_decoder": true
+    },
+    "tokenizer_name": "facebook/bart-large",
+    "tokenizer_max_length": 1024,
+    "tokenizer_padding_side": "right",
+    "pad_language_to": "max_length",
+    "hidden_size": 1024,
+    "depth": 24,
+    "num_heads": 16,
+    "mlp_ratio": 4.0,
+    "num_domains": 30,
+    "len_soft_prompts": 32,
+    "dim_time": 32,
+    "max_len_seq": 512,
+    "use_hetero_proj": false,
+    "action_mode": "ee6d",
+    "num_denoising_steps": 10,
+    "use_proprio": true,
+    "max_state_dim": 20,
+    "domain_feature_key": null,
+    "resize_imgs_with_padding": [
+        224,
+        224
+    ],
+    "num_image_views": 3,
+    "empty_cameras": 1,
+    "optimizer_lr": 0.0001,
+    "optimizer_betas": [
+        0.9,
+        0.95
+    ],
+    "optimizer_eps": 1e-08,
+    "optimizer_weight_decay": 0.0001,
+    "optimizer_grad_clip_norm": 10.0,
+    "scheduler_warmup_steps": 1000,
+    "scheduler_decay_steps": 30000,
+    "scheduler_decay_lr": 2.5e-06
+}

model.safetensors

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policy_postprocessor.json

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+{
+  "name": "policy_postprocessor",
+  "steps": [
+    {
+      "registry_name": "unnormalizer_processor",
+      "config": {
+        "eps": 1e-08,
+        "features": {
+          "action": {
+            "type": "ACTION",
+            "shape": [
+              20
+            ]
+          }
+        },
+        "norm_map": {
+          "STATE": "IDENTITY",
+          "ACTION": "MEAN_STD",
+          "VISUAL": "MEAN_STD"
+        }
+      }
+    },
+    {
+      "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": "tokenizer_processor",
+      "config": {
+        "max_length": 50,
+        "task_key": "task",
+        "padding_side": "right",
+        "padding": "max_length",
+        "truncation": true,
+        "tokenizer_name": "facebook/bart-large"
+      }
+    },
+    {
+      "registry_name": "device_processor",
+      "config": {
+        "device": "cuda",
+        "float_dtype": null
+      }
+    },
+    {
+      "registry_name": "normalizer_processor",
+      "config": {
+        "eps": 1e-08,
+        "features": {
+          "observation.images.image": {
+            "type": "VISUAL",
+            "shape": [
+              3,
+              256,
+              256
+            ]
+          },
+          "observation.images.image2": {
+            "type": "VISUAL",
+            "shape": [
+              3,
+              256,
+              256
+            ]
+          },
+          "observation.state": {
+            "type": "STATE",
+            "shape": [
+              8
+            ]
+          },
+          "observation.images.empty_camera_0": {
+            "type": "VISUAL",
+            "shape": [
+              3,
+              224,
+              224
+            ]
+          },
+          "action": {
+            "type": "ACTION",
+            "shape": [
+              20
+            ]
+          }
+        },
+        "norm_map": {
+          "STATE": "IDENTITY",
+          "ACTION": "MEAN_STD",
+          "VISUAL": "IDENTITY"
+        }
+      }
+    }
+  ]
+}