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Running on Zero
| title: ERVLA | |
| emoji: 🤖 | |
| colorFrom: gray | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 6.17.3 | |
| app_file: app.py | |
| short_description: Camera views + instruction → robot action chunks | |
| python_version: "3.12" | |
| startup_duration_timeout: 1h | |
| # ERVLA: Embodied Chain-of-Thought for Generalizable Robot Manipulation | |
| Demo of **ERVLA** (`ERVLA/liberoplus`) — a vision-language-action (VLA) manipulation policy | |
| built on a Qwen3-VL-4B backbone with a DiT flow-matching action head (MiBoT). The | |
| `liberoplus` checkpoint is the LIBERO-Plus-tuned model that reaches 86.9% success on the | |
| LIBERO-Plus benchmark. | |
| **What the Space does:** you upload the two camera observations a LIBERO robot would see | |
| (base/agentview + left wrist), type the task instruction, and the model predicts the next | |
| end-effector **action chunk** — 10 steps of Δxyz translation, Δrotation, and gripper | |
| command. The chunk is shown as a line chart of the translation deltas plus a full table | |
| (and CSV) of all 7 action dimensions per step. | |
| **No simulator runs here.** LIBERO-Plus/VLABench environments cannot be deployed inside a | |
| Space, so the predicted action chunk is the demo's output — the same_tensor the reference | |
| deployment server streams to the LIBERO evaluator. | |
| ## Inputs | |
| - **Base view** — the agentview camera image (LIBERO observation convention). | |
| - **Left-wrist view** — the wrist camera image. | |
| - **Instruction** — the task, e.g. *"Turn on the stove and put the moka pot on it."* | |
| - **Advanced:** LIBERO suite (robot type), the robot's 8-dim proprioceptive state | |
| (EE position, orientation as axis-angle, gripper finger joints), sampling seed, and | |
| chunk length. | |
| ## How it maps to the reference implementation | |
| The inference path replicates `src/server/deploy_hf_liberoplus.py` from the | |
| [ERVLA repo](https://github.com/Oscar-Yang11/ERVLA): the same multi-view chat template | |
| (`/no_cot`), the raw state zero-padded to the model's 60-dim state window, the manual | |
| action mask over dims 0–6, 5-step Euler integration of the flow-matching head, and | |
| denormalization with the `libero_all` stats from `configs/data/sources/libero.yaml`. | |
| ## Credits & data | |
| - Paper: [Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation](https://huggingface.co/papers/2606.03784) | |
| - Code: [Oscar-Yang11/ERVLA](https://github.com/Oscar-Yang11/ERVLA) | |
| - Model: [ERVLA/liberoplus](https://huggingface.co/ERVLA/liberoplus) (also | |
| [ERVLA/pretrain](https://huggingface.co/ERVLA/pretrain), | |
| [ERVLA/vlabench](https://huggingface.co/ERVLA/vlabench)) | |
| - The bundled example frames (`ex_*.png`) are extracted from the authors' released | |
| [ERVLA/Libero](https://huggingface.co/datasets/ERVLA/Libero) demonstration dataset and | |
| are shown here for demonstration of the model's own training-domain inputs; all credit | |
| for those images goes to the ERVLA authors. |