README.md

metadata

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: 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
• Code: Oscar-Yang11/ERVLA
• Model: ERVLA/liberoplus (also ERVLA/pretrain, ERVLA/vlabench)
• The bundled example frames (ex_*.png) are extracted from the authors' released 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.