Add pi0 G2 dual-arm checkpoint (30k steps) + model card

.gitattributes

@@ -207,3 +207,20 @@ dataset/g2_dualarm_yogurt_rs10/videos/chunk-000/observation.images.head/episode_
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README.md

@@ -0,0 +1,113 @@
+---
+license: apache-2.0
+tags:
+  - robotics
+  - vla
+  - openpi
+  - pi0
+  - lerobot
+  - bimanual
+  - manipulation
+library_name: openpi
+pipeline_tag: robotics
+---
+
+# G2 Dual-Arm — π₀ policy + LeRobot dataset
+
+A π₀ (pi0) vision-language-action policy trained on an **AgiBot G2** dual-arm robot, together
+with the dataset it was trained on. The policy runs closed-loop on the real robot from live
+camera observations and completes the task.
+
+**Task:** `Make a yogurt bowl with cereal and fruit.`
+
+| | |
+|---|---|
+| Robot | AgiBot G2, dual 7-DOF arms + 2 grippers |
+| Action / state | 16-D `[L_j1..j7, L_grip, R_j1..j7, R_grip]`, **absolute joint radians** + boolean gripper |
+| Cameras | `head` (400×640), `hand_left` / `hand_right` (528×640), RGB |
+| Control rate | **10 Hz** (dataset fps); robot servo runs at 30 Hz |
+| Action chunk | 50 |
+
+## Contents
+
+```
+params/                       π₀ weights (12 GB, JAX/orbax)
+assets/                       norm stats bundled with the checkpoint
+_CHECKPOINT_METADATA
+dataset/g2_dualarm_yogurt_rs10/   LeRobot v2.1 — 58 episodes / 48,332 frames / 10 Hz
+```
+
+## Training
+
+| | |
+|---|---|
+| Config | `pi0_g2_dualarm` (openpi) |
+| Steps | 30,000, batch 32, 1× H200 |
+| Wall time | 17 h 06 m |
+| Final loss | 0.0047 (from 0.3207) |
+
+Actions are converted to deltas at train time via `make_bool_mask(7, -1, 7, -1)` — 7 joint
+dims as deltas, gripper kept absolute, per arm.
+
+## Dataset notes (read before training on it)
+
+* **`_rs10` = resampled to a uniform 10 Hz grid.** The raw recordings are badly jittered
+  (only 13.6% of inter-frame gaps fall within ±20% of the median) because the on-robot
+  *disk writer* — not the cameras or the control loop — is the bottleneck: the control loop
+  runs at a rock-steady 29.5–29.9 Hz and the cameras at ~30 Hz, but 60–65% of frames are
+  dropped before they reach disk. Naive contiguous re-indexing distorts episode duration by
+  −18%…+35%; the resampled variant is accurate to +0.06%.
+* **The right gripper's position sensor is dead** (constant 0.2173 across all 60 episodes),
+  so both gripper dims come from the *command* boolean, not from a sensor.
+* **Gripper polarity in this dataset: `1.0` = OPEN, `0.0` = CLOSED.** The underlying raw
+  field is named `gripper_closed`, and **the name is backwards relative to the physical
+  behaviour**. Verified from the physical semantics: in episode 27 the right gripper dim
+  flips `1.0 → 0.0` at step 54, exactly as the right arm reaches its deepest point — the
+  grasp. If you drive real hardware, confirm your wire convention separately; on this robot
+  the DDS tool wire is the opposite (`1.0` = CLOSED), so the mapping is an inversion.
+* 2 of 60 recorded episodes are excluded (camera dropouts mid-episode, broken on the robot).
+
+## Evaluation
+
+Offline replay against a live server, 120 frames over 12 episodes:
+
+| metric | value | threshold |
+|---|---|---|
+| joint MAE | **0.28°** (worst 0.95°) | < 5° |
+| gripper agreement | **99.8%** | > 90% |
+| delta direction cosine | **0.999** | > 0.8 |
+| first-step jump | 1.47° (worst 5.43°) | worst < 15° |
+
+On real hardware the policy was validated in four stages — dry-run → ground-truth replay →
+policy-replay on dataset observations → live closed loop — and completes the task in the
+last stage.
+
+## Using the weights
+
+```bash
+# serve (openpi)
+python examples/g2_dualarm/g2_serve_openpi.py \
+    --config pi0_g2_dualarm --ckpt-dir <this repo>/  --port 8000
+```
+
+The server publishes the deployment contract in its connect-time metadata:
+`action_chunk_size=50`, `action_rate_hz=10.0`, `robot_servo_hz=30.0`,
+`action_space="absolute_joint_rad_plus_gripper_cmd"`, plus a `reset_pose`.
+
+> ⚠️ **Advance the chunk at 10 Hz, not at the servo rate.** Actions are absolute joint
+> targets; stepping them at 30 Hz executes the trajectory 3× too fast.
+
+## Caveats
+
+* Single task, single scene, 58 demonstrations. Expect it to be brittle outside the
+  recorded prop layout.
+* The policy is stochastic (flow matching): identical input gives joint outputs differing
+  by ~1.2°. Do not use repeated inference as a determinism check.
+* **Do not JPEG-compress observations to save bandwidth.** Measured on this checkpoint,
+  JPEG q92/q85 flips the *gripper decision* outright (|Δ| = 1.007 on a boolean dim, 3 of 6
+  trials) while joint dims move only ~2°. The mean error hides this completely.
+
+## Code
+
+Evaluation stack, real-robot client and the staged validation tooling:
+`examples/g2_dualarm/` in the accompanying openpi fork.

_CHECKPOINT_METADATA

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assets/g2_dualarm_yogurt_rs10/norm_stats.json

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params/array_metadatas/process_0

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