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README.md

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+---
+license: apache-2.0
+tags:
+- robotics
+- manipulation
+- vla
+- robopro
+- robotwin
+- evaluation
+---
+
+# DA3-VLA — RoboPRO Evaluation Results
+
+Full evaluation results on the **RoboPRO** benchmark (RoboTwin / SAPIEN sim, 79 tasks across
+kitchenl / kitchens / office / study), organized **by model**. Each model folder contains the
+scored metrics, per-task CSVs, rollout videos, and the model checkpoint.
+
+Protocol: stock TOPP control; **clean** = 20 seeds/task, **clutter** = d6–d15 × 2 seeds/task.
+Clean and clutter are reported separately (never averaged).
+
+## Headline numbers
+
+| Model | Clean SR / HSR | Clutter SR / HSR |
+|---|---|---|
+| **pi05_da3_v2final_ckpt39999** — pi0.5 + DA3 (L6-Action XAttn), 30/50 exec | **81.3 / 62.8** | **72.8 / 25.1** |
+| **pi05_base_50-50** — vanilla pi0.5 (clean 50/50, clutter 30/50) | 70.1 / 54.9 | 62.2 / 22.9 |
+| **da3-xvla-45k** — giant X-VLA + DA3 (L6-Action XAttn), ckpt 45k | 55.6 / 46.7 | 45.3 / 11.2* |
+
+*da3-xvla-45k clutter is partial (n=1187, ~75%). The two pi05 models are complete and matched.
+**DA3 beats vanilla pi0.5 on both configs: clean +11.2/+7.9, clutter +10.6/+2.2.**
+
+## Layout (per model)
+
+```
+<model>/
+  SUMMARY.txt              # TOTAL + per-scene + per-task, for CLEAN and CLUTTER sections
+  per_task_clean.csv       # scene,task,SR,HSR,n
+  per_task_clutter.csv     # scene,task,SR,HSR,n
+  <scene>__<task>/         # rollout videos: episodeN.mp4 (clean), d<N>_episodeN.mp4 (clutter)
+  checkpoint/              # the model weights used for this eval
+```
+
+Models:
+- `pi05_da3_v2final_ckpt39999` — pi0.5 + DA3 spatial addon, v2-final checkpoint (openpi/JAX).
+- `pi05_base_50-50` — base pi0.5 (jax 30k), the vanilla baseline.
+- `da3-xvla-45k` — `v1-nested-giant-perc-ckpt45000`, giant X-VLA with DA3 (policy `dxvla`).
+
+## Notes
+- SR = success rate; HSR = hard success (success with **zero** collisions).
+- Sim: SAPIEN 3.0.0b1 on GB10 (aarch64). Long-horizon kitchens chains and dense clutter are the
+  hardest cells; clutter HSR is collision-bottlenecked for all models.

pi05_base_50-50/SUMMARY.txt

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+pi05_base_50-50
+benchmark: robopro
+clean source: robopro30k_topp_    clutter source: final_pi05_
+clean vs clutter reported separately — never averaged.
+generated by build_final_eval.py
+
+==============================================================================
+CLEAN
+==============================================================================
+
+TOTAL
+  SR 70.1%   HSR 54.9%   episodes=1530
+
+PER-SCENE
+  kitchenl  SR   77.8%   HSR   57.0%   n=400
+  kitchens  SR   66.7%   HSR   42.5%   n=369
+  office    SR   56.6%   HSR   42.9%   n=380
+  study     SR   78.7%   HSR   76.6%   n=381
+
+PER-TASK
+  scene     task                                              SR     HSR     n
+  kitchenl  move_bottle                                    50.0%   50.0%  n=  20
+  kitchenl  move_bottle_from_fridge_next_to_can           100.0%    0.0%  n=  20
+  kitchenl  move_can_from_cabinet_to_basket                10.0%   10.0%  n=  20
+  kitchenl  move_milk_close_fridge                         40.0%    0.0%  n=  20
+  kitchenl  pick_bottle_from_fridge                       100.0%  100.0%  n=  20
+  kitchenl  pick_boxdrink_from_basket                      50.0%    5.0%  n=  20
+  kitchenl  pick_can_from_basket                           85.0%   35.0%  n=  20
+  kitchenl  pick_can_from_cabinet                         100.0%   95.0%  n=  20
+  kitchenl  pick_milk_box_from_fridge                      85.0%   85.0%  n=  20
+  kitchenl  pick_sauce_can_from_cabinet                   100.0%  100.0%  n=  20
+  kitchenl  put_bottle_in_basket                           90.0%   90.0%  n=  20
+  kitchenl  put_bottle_in_fridge                           75.0%   75.0%  n=  20
+  kitchenl  put_can_close_cabinet                          95.0%   95.0%  n=  20
+  kitchenl  put_can_in_cabinet                             80.0%   65.0%  n=  20
+  kitchenl  put_can_infront_of_microwave                   65.0%   60.0%  n=  20
+  kitchenl  put_can_next_to_basket                         90.0%   70.0%  n=  20
+  kitchenl  put_milk_box_in_fridge                         85.0%   75.0%  n=  20
+  kitchenl  put_sauce_can_in_basket                        90.0%   40.0%  n=  20
+  kitchenl  put_sauce_can_in_cabinet                       90.0%   85.0%  n=  20
+  kitchenl  switch_can_with_bottle_in_basket               75.0%    5.0%  n=  20
+  kitchens  chain_apple_bin_bowl_rack_spoon_sink_ks         0.0%    0.0%  n=  13
+  kitchens  chain_apple_sink_plate_bread_board_ks         100.0%    0.0%  n=  20
+  kitchens  chain_bowl_rack_apple_sink_ks                   0.0%    0.0%  n=  16
+  kitchens  chain_heat_hamburger_ks                        60.0%   60.0%  n=  20
+  kitchens  chain_serve_hamburger_ks                       70.0%   70.0%  n=  20
+  kitchens  close_microwave_ks                             55.0%   55.0%  n=  20
+  kitchens  drop_apple_in_bin_ks                           70.0%    5.0%  n=  20
+  kitchens  move_hamburger_onto_plate_ks                   70.0%   70.0%  n=  20
+  kitchens  pick_apple_from_bowl_ks                        95.0%    0.0%  n=  20
+  kitchens  pick_apple_from_sink_ks                       100.0%  100.0%  n=  20
+  kitchens  pick_fork_from_sink_ks                        100.0%   65.0%  n=  20
+  kitchens  pick_hamburger_from_microwave_ks               90.0%   90.0%  n=  20
+  kitchens  place_bowl_in_dishrack_ks                       5.0%    5.0%  n=  20
+  kitchens  put_bowl_in_sink_ks                            95.0%   95.0%  n=  20
+  kitchens  put_bread_on_board_ks                          85.0%    5.0%  n=  20
+  kitchens  put_hamburger_in_microwave_ks                  55.0%   50.0%  n=  20
+  kitchens  put_spoon_in_dishrack_ks                       15.0%   15.0%  n=  20
+  kitchens  put_spoon_in_sink_ks                          100.0%  100.0%  n=  20
+  kitchens  put_spoon_on_plate_ks                          65.0%    0.0%  n=  20
+  office    close_drawer                                  100.0%  100.0%  n=  20
+  office    move_items_around                              30.0%   20.0%  n=  20
+  office    open_drawer                                    40.0%   40.0%  n=  20
+  office    organize_table                                 10.0%   10.0%  n=  10
+  office    put_book_in_fileholder                         30.0%   15.0%  n=  20
+  office    put_book_on_book                               35.0%   35.0%  n=  20
+  office    put_milktea_next_to_laptop                     65.0%   50.0%  n=  20
+  office    put_milktea_on_shelf                           20.0%   20.0%  n=  20
+  office    put_mouse_next_to_stapler                      55.0%    0.0%  n=  20
+  office    put_mouse_on_pad                               65.0%   65.0%  n=  20
+  office    put_phone_next_to_cube                         85.0%   60.0%  n=  20
+  office    put_phone_on_holder                            80.0%   80.0%  n=  20
+  office    put_rubikscube_in_drawer                       50.0%   50.0%  n=  20
+  office    put_rubikscube_next_to_milktea                 40.0%    0.0%  n=  20
+  office    put_stapler_in_drawer                          90.0%   85.0%  n=  20
+  office    put_stapler_next_to_mouse                      80.0%    0.0%  n=  20
+  office    put_stapler_on_book                           100.0%  100.0%  n=  20
+  office    set_up_table                                   20.0%    0.0%  n=  10
+  office    store_rubikscube_on_shelf                      35.0%   30.0%  n=  20
+  office    store_stapler_in_drawer                        60.0%   60.0%  n=  20
+  study     empty_box                                      95.0%   95.0%  n=  20
+  study     move_book_onto_table                          100.0%   95.0%  n=  20
+  study     move_cup                                       80.0%   80.0%  n=  20
+  study     move_cup_next_to_book                          75.0%   75.0%  n=  20
+  study     move_cup_onto_table                            80.0%   80.0%  n=  20
+  study     move_cup_put_pen_in_cup                        45.0%   45.0%  n=  20
+  study     move_cups_into_box                             57.1%   57.1%  n=  14
+  study     move_pen_to_box                                90.9%   90.9%  n=  11
+  study     move_seal_cup_next_to_box                      62.5%   62.5%  n=  16
+  study     move_seal_next_to_box                          80.0%   80.0%  n=  20
+  study     move_seal_next_to_pencup                       85.0%   85.0%  n=  20
+  study     move_seal_onto_book                           100.0%   95.0%  n=  20
+  study     move_seal_onto_table                          100.0%  100.0%  n=  20
+  study     put_cup_in_box                                 90.0%   90.0%  n=  20
+  study     put_cup_on_coaster                             45.0%   45.0%  n=  20
+  study     put_cup_on_table                              100.0%  100.0%  n=  20
+  study     put_glue_in_box                                90.0%   70.0%  n=  20
+  study     put_pen_in_box                                 80.0%   80.0%  n=  20
+  study     put_pen_in_pencup                              15.0%    5.0%  n=  20
+  study     put_seal_in_box                               100.0%  100.0%  n=  20
+
+==============================================================================
+CLUTTER (d6..d15)
+==============================================================================
+
+TOTAL
+  SR 62.2%   HSR 22.9%   episodes=1573
+
+PER-SCENE
+  kitchenl  SR   67.5%   HSR   11.8%   n=400
+  kitchens  SR   66.6%   HSR   26.7%   n=374
+  office    SR   46.0%   HSR   22.0%   n=400
+  study     SR   69.2%   HSR   31.3%   n=399
+
+PER-TASK
+  scene     task                                              SR     HSR     n
+  kitchenl  move_bottle                                    50.0%   30.0%  n=  20
+  kitchenl  move_bottle_from_fridge_next_to_can           100.0%    0.0%  n=  20
+  kitchenl  move_can_from_cabinet_to_basket                60.0%   15.0%  n=  20
+  kitchenl  move_milk_close_fridge                         30.0%    0.0%  n=  20
+  kitchenl  pick_bottle_from_fridge                       100.0%   25.0%  n=  20
+  kitchenl  pick_boxdrink_from_basket                       0.0%    0.0%  n=  20
+  kitchenl  pick_can_from_basket                           95.0%    0.0%  n=  20
+  kitchenl  pick_can_from_cabinet                          90.0%   15.0%  n=  20
+  kitchenl  pick_milk_box_from_fridge                     100.0%   50.0%  n=  20
+  kitchenl  pick_sauce_can_from_cabinet                   100.0%    0.0%  n=  20
+  kitchenl  put_bottle_in_basket                           60.0%    5.0%  n=  20
+  kitchenl  put_bottle_in_fridge                           15.0%    0.0%  n=  20
+  kitchenl  put_can_close_cabinet                          85.0%    0.0%  n=  20
+  kitchenl  put_can_in_cabinet                             60.0%   10.0%  n=  20
+  kitchenl  put_can_infront_of_microwave                   50.0%   35.0%  n=  20
+  kitchenl  put_can_next_to_basket                         65.0%   15.0%  n=  20
+  kitchenl  put_milk_box_in_fridge                         70.0%    5.0%  n=  20
+  kitchenl  put_sauce_can_in_basket                        85.0%   10.0%  n=  20
+  kitchenl  put_sauce_can_in_cabinet                       75.0%   20.0%  n=  20
+  kitchenl  switch_can_with_bottle_in_basket               60.0%    0.0%  n=  20
+  kitchens  chain_apple_bin_bowl_rack_spoon_sink_ks        85.0%    0.0%  n=  20
+  kitchens  chain_apple_sink_plate_bread_board_ks          95.0%    0.0%  n=  20
+  kitchens  chain_bowl_rack_apple_sink_ks                   0.0%    0.0%  n=  20
+  kitchens  chain_heat_hamburger_ks                        68.4%   52.6%  n=  19
+  kitchens  chain_serve_hamburger_ks                       40.0%   33.3%  n=  15
+  kitchens  close_microwave_ks                             95.0%   65.0%  n=  20
+  kitchens  drop_apple_in_bin_ks                           65.0%    0.0%  n=  20
+  kitchens  move_hamburger_onto_plate_ks                   70.0%   65.0%  n=  20
+  kitchens  pick_apple_from_bowl_ks                        65.0%    0.0%  n=  20
+  kitchens  pick_apple_from_sink_ks                        85.0%   45.0%  n=  20
+  kitchens  pick_fork_from_sink_ks                        100.0%   70.0%  n=  20
+  kitchens  pick_hamburger_from_microwave_ks               70.0%   15.0%  n=  20
+  kitchens  place_bowl_in_dishrack_ks                       0.0%    0.0%  n=  20
+  kitchens  put_bowl_in_sink_ks                            95.0%   50.0%  n=  20
+  kitchens  put_bread_on_board_ks                          70.0%    5.0%  n=  20
+  kitchens  put_hamburger_in_microwave_ks                  45.0%   25.0%  n=  20
+  kitchens  put_spoon_in_dishrack_ks                       30.0%    5.0%  n=  20
+  kitchens  put_spoon_in_sink_ks                          100.0%   80.0%  n=  20
+  kitchens  put_spoon_on_plate_ks                          80.0%    0.0%  n=  20
+  office    close_drawer                                  100.0%  100.0%  n=  20
+  office    move_items_around                              10.0%    0.0%  n=  20
+  office    open_drawer                                    85.0%   80.0%  n=  20
+  office    organize_table                                  0.0%    0.0%  n=  20
+  office    put_book_in_fileholder                         20.0%    5.0%  n=  20
+  office    put_book_on_book                               40.0%   35.0%  n=  20
+  office    put_milktea_next_to_laptop                     65.0%    5.0%  n=  20
+  office    put_milktea_on_shelf                           10.0%    0.0%  n=  20
+  office    put_mouse_next_to_stapler                      80.0%    0.0%  n=  20
+  office    put_mouse_on_pad                               45.0%   20.0%  n=  20
+  office    put_phone_next_to_cube                         50.0%    0.0%  n=  20
+  office    put_phone_on_holder                            85.0%   10.0%  n=  20
+  office    put_rubikscube_in_drawer                       55.0%   50.0%  n=  20
+  office    put_rubikscube_next_to_milktea                 45.0%   25.0%  n=  20
+  office    put_stapler_in_drawer                          65.0%   25.0%  n=  20
+  office    put_stapler_next_to_mouse                       0.0%    0.0%  n=  20
+  office    put_stapler_on_book                            90.0%   70.0%  n=  20
+  office    set_up_table                                    5.0%    0.0%  n=  20
+  office    store_rubikscube_on_shelf                      10.0%    0.0%  n=  20
+  office    store_stapler_in_drawer                        60.0%   15.0%  n=  20
+  study     empty_box                                      65.0%   40.0%  n=  20
+  study     move_book_onto_table                           90.0%   55.0%  n=  20
+  study     move_cup                                       45.0%   20.0%  n=  20
+  study     move_cup_next_to_book                          30.0%    0.0%  n=  20
+  study     move_cup_onto_table                            65.0%   15.0%  n=  20
+  study     move_cup_put_pen_in_cup                        60.0%    5.0%  n=  20
+  study     move_cups_into_box                             60.0%   10.0%  n=  20
+  study     move_pen_to_box                                78.9%    0.0%  n=  19
+  study     move_seal_cup_next_to_box                     100.0%   90.0%  n=  20
+  study     move_seal_next_to_box                          95.0%   85.0%  n=  20
+  study     move_seal_next_to_pencup                       75.0%   45.0%  n=  20
+  study     move_seal_onto_book                            85.0%   25.0%  n=  20
+  study     move_seal_onto_table                          100.0%    0.0%  n=  20
+  study     put_cup_in_box                                 35.0%   35.0%  n=  20
+  study     put_cup_on_coaster                             30.0%    0.0%  n=  20
+  study     put_cup_on_table                               90.0%   15.0%  n=  20
+  study     put_glue_in_box                                95.0%   30.0%  n=  20
+  study     put_pen_in_box                                 95.0%   65.0%  n=  20
+  study     put_pen_in_pencup                               0.0%    0.0%  n=  20
+  study     put_seal_in_box                                90.0%   90.0%  n=  20
+
+PER-LEVEL
+  d6   SR   60.1%  HSR   27.8%  n=158
+  d7   SR   62.7%  HSR   24.7%  n=158
+  d8   SR   63.5%  HSR   25.6%  n=156
+  d9   SR   62.2%  HSR   27.6%  n=156
+  d10  SR   63.3%  HSR   22.8%  n=158
+  d11  SR   60.8%  HSR   20.9%  n=158
+  d12  SR   61.5%  HSR   19.2%  n=156
+  d13  SR   63.9%  HSR   23.4%  n=158
+  d14  SR   59.2%  HSR   15.3%  n=157
+  d15  SR   65.2%  HSR   21.5%  n=158

pi05_base_50-50/checkpoint/.gitattributes

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pi05_base_50-50/checkpoint/_CHECKPOINT_METADATA

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+{"item_handlers": {"assets": "openpi.training.checkpoints.CallbackHandler", "params": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler", "train_state": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler"}, "metrics": {}, "performance_metrics": {}, "init_timestamp_nsecs": 1783698321223597218, "commit_timestamp_nsecs": 1783698377888963486, "custom": {}}

pi05_base_50-50/checkpoint/model_params.md

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+# RoboPRO π₀.₅ (JAX) — step 30000 checkpoint
+
+Fine-tuned **π₀.₅ (pi05)** VLA policy for the **Aloha-Agilex** bimanual robot, trained with [openpi](https://github.com/Physical-Intelligence/openpi) (JAX/Flax) on the RoboPRO **top-cam** dataset (`roboreal_lerobot`). This repo holds the **eval weights only** (no optimizer state).
+
+- **Base model:** `pi05_base` (Physical Intelligence), ~3.6B params
+- **Framework:** JAX / Flax, orbax checkpoint (this is **not** a PyTorch/safetensors checkpoint)
+- **Precision:** bfloat16
+- **Training:** 30,000 steps, global batch 192, cosine LR (peak 2.5e-5), ~1.5 epochs over 3.74M frames @ 25 Hz
+- **Final train loss:** ~0.0021 (flow-matching)
+
+---
+
+## Repo contents
+
+```
+params/                         # orbax model weights (load these)
+assets/roboreal_lerobot/
+    norm_stats.json             # input/output normalization stats (REQUIRED)
+_CHECKPOINT_METADATA
+```
+> ⚠️ `train_state/` (optimizer) is **not** included — this checkpoint is for **inference/eval only**, not for resuming training.
+
+---
+
+## Inputs
+
+The policy consumes a single-timestep observation dict with **3 camera images + a 14-D robot state + a language prompt**.
+
+### 1. Cameras (3× RGB)
+| policy key | physical view | shape | dtype |
+|---|---|---|---|
+| `cam_high` | **overhead / countertop** camera (looking down at the table) | `[3, H, W]` (CHW) | `uint8`, 0–255 |
+| `cam_left_wrist` | left-arm wrist camera | `[3, H, W]` | `uint8`, 0–255 |
+| `cam_right_wrist` | right-arm wrist camera | `[3, H, W]` | `uint8`, 0–255 |
+
+- **RGB**, channel-first `[3, H, W]`. Images are internally resized to **224×224**, so any input resolution works (training used 240×320).
+- **Camera mapping is critical:** feed your **countertop/overhead** view as `cam_high` (the model was trained with the top-cam view in that slot, *not* a robot-head camera). Wrist cams map by side.
+- All three cameras are required.
+
+### 2. State — `state`
+- `float32[14]`, raw joint positions (radians) + gripper, **absolute**, in Aloha convention.
+- Order (same for state and action):
+  ```
+  0 left_waist    1 left_shoulder  2 left_elbow      3 left_forearm_roll
+  4 left_wrist_angle  5 left_wrist_rotate  6 left_gripper
+  7 right_waist   8 right_shoulder 9 right_elbow    10 right_forearm_roll
+  11 right_wrist_angle 12 right_wrist_rotate 13 right_gripper
+  ```
+- Feed **raw physical values** — normalization (quantile, from `norm_stats.json`) and the Aloha→pi convention conversion happen **inside** the policy.
+
+### 3. Prompt — `prompt`
+- Natural-language task instruction, e.g. `"put the mouse on the pad"`. Trained on 1,622 instruction variants across 80 tasks.
+
+### Observation dict shape
+```python
+observation = {
+    "state": np.ndarray,          # float32 [14]
+    "images": {
+        "cam_high":       np.ndarray,  # uint8 [3, H, W]  (countertop)
+        "cam_left_wrist": np.ndarray,  # uint8 [3, H, W]
+        "cam_right_wrist":np.ndarray,  # uint8 [3, H, W]
+    },
+    "prompt": str,
+}
+```
+
+---
+
+## Output
+
+`policy.infer(observation)["actions"]` returns an **action chunk**:
+
+- Shape **`[50, 14]`** — 50 future timesteps (`action_horizon=50`), 14-D per step.
+- **Absolute joint-position targets** in Aloha convention, same 14-D order as `state`.
+- De-normalized to physical units (you feed raw, you get raw).
+- At **25 Hz**, the 50-step chunk ≈ 2 s of motion. Typical control: execute the first *k* actions (e.g. `pi0_step` steps), then re-infer with the new observation.
+
+### Why the output is absolute (delta vs. absolute)
+
+This config trains with `use_delta_joint_actions = True`, which installs a paired transform around the model:
+
+- **Training input** — `DeltaActions(mask)`: `actions[:, :dims] -= where(mask, state, 0)`  → masked dims become **(target − current_state)** = deltas.
+- **Inference output** — `AbsoluteActions(mask)`: `actions[:, :dims] += where(mask, state, 0)`  → masked dims become **(delta + current_state)** = absolute.
+
+The mask is `make_bool_mask(6, -1, 6, -1)` = `[True×6, False, True×6, False]`:
+
+| dims | joints | mask | model learns | returned |
+|---|---|---|---|---|
+| 0–5, 7–12 | 6 arm joints per arm | `True` | **delta** | **absolute** (state re-added on output) |
+| 6, 13 | grippers | `False` | absolute | absolute |
+
+So the network *internally* predicts arm-joint **deltas**, but `AbsoluteActions` runs on the output and adds back the observation's `state`, so the policy returns **absolute joint-position targets**. Grippers are absolute throughout.
+
+**Practical implications for eval:**
+- Send the returned `actions` **directly** as target joint positions — do **not** add the current state yourself; the output transform already did.
+- `AbsoluteActions` broadcasts the *single* observation `state` across all 50 timesteps, so every action in the chunk is absolute relative to the `state` you passed at that inference call (standard openpi behavior).
+- The `state` you feed therefore affects the arm outputs (it's the base the deltas are added to); feed the robot's true current joint positions.
+
+---
+
+## How to run inference (openpi, JAX)
+
+Requires an openpi env with **JAX** (this project's `pi05` conda env) and the `pi05_robopro_top_cam_jax` train config (defines the repack + Aloha transforms + norm stats binding). The exact config is included in this repo as **`train_config.py`** — paste its `TrainConfig(...)` entry into the `_CONFIGS` list in your openpi `src/openpi/training/config.py`.
+
+```python
+from openpi.policies import policy_config as _policy_config
+from openpi.training import config as _config
+
+train_config = _config.get_config("pi05_robopro_top_cam_jax")
+
+# checkpoint_dir must contain params/ and assets/ (this repo's root after download)
+policy = _policy_config.create_trained_policy(
+    train_config,
+    "/path/to/robopro_jax_30000",     # dir with params/ + assets/
+    robotwin_repo_id="roboreal_lerobot",  # picks assets/roboreal_lerobot/norm_stats.json
+)
+
+# Build the observation (feed COUNTERTOP cam as cam_high; images CHW uint8)
+obs = {
+    "state": state_14,                       # float32[14], absolute joints
+    "images": {
+        "cam_high":        countertop_chw,   # uint8[3,H,W]
+        "cam_left_wrist":  left_chw,
+        "cam_right_wrist": right_chw,
+    },
+    "prompt": "put the mouse on the pad",
+}
+
+actions = policy.infer(obs)["actions"]   # np.ndarray [50, 14], absolute joint targets
+# execute actions[:k] on the robot, then re-infer
+```
+
+Notes:
+- Loading is **auto-detected** as JAX because the checkpoint has `params/` (not `model.safetensors`).
+- If your runtime provides differently-named observation keys, apply a repack so images land under `cam_high` / `cam_left_wrist` / `cam_right_wrist`, state under `state`, and set `prompt`.
+- `norm_stats.json` **must** be present/loaded; without it actions are unnormalized and wrong.

pi05_base_50-50/checkpoint/params/_METADATA

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pi05_base_50-50/checkpoint/train_config.py

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+# Train / eval config for this checkpoint: `pi05_robopro_top_cam_jax`
+#
+# This is the exact openpi TrainConfig used to fine-tune and to load this
+# checkpoint. To use it, add the TrainConfig(...) entry below to the `_CONFIGS`
+# list in `src/openpi/training/config.py` of your openpi checkout, then load it
+# with `openpi.training.config.get_config("pi05_robopro_top_cam_jax")`.
+#
+# Symbols referenced (already imported at the top of openpi's config.py):
+#   TrainConfig, DataConfig, LeRobotAlohaDataConfig
+#   pi0_config            = openpi.models.pi0_config
+#   _transforms           = openpi.transforms
+#   weight_loaders        = openpi.training.weight_loaders
+#   _optimizer            = openpi.training.optimizer
+#
+# Dataset: robopro top-cam LeRobot v2.1 (`roboreal_lerobot`), robot_type roboreal,
+#   25 fps, 14-DoF dual-arm, cams countertop/left/right. Set
+#   HF_LEROBOT_HOME=<parent> so repo_id `roboreal_lerobot` resolves locally.
+# Base weights: JAX pi05_base from gs://openpi-assets (auto-download).
+
+TrainConfig(
+    name="pi05_robopro_top_cam_jax",
+    model=pi0_config.Pi0Config(pi05=True),
+    data=LeRobotAlohaDataConfig(
+        repo_id="roboreal_lerobot",
+        # Map the dataset's raw feature keys -> the model's expected keys.
+        # NOTE: cam_high is fed from the COUNTERTOP (overhead) camera.
+        repack_transforms=_transforms.Group(inputs=[
+            _transforms.RepackTransform({
+                "images": {
+                    "cam_high": "observation.images.countertop",
+                    "cam_left_wrist": "observation.images.left",
+                    "cam_right_wrist": "observation.images.right",
+                },
+                "state": "observation.state",
+                "actions": "action",
+                "prompt": "prompt",
+            })
+        ]),
+        base_config=DataConfig(
+            prompt_from_task=True,
+        ),
+        # (defaults inherited from LeRobotAlohaDataConfig:)
+        #   adapt_to_pi=True, use_delta_joint_actions=True
+        #   -> arm joints trained as delta, grippers absolute;
+        #      AbsoluteActions on output => returned actions are ABSOLUTE.
+    ),
+    weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+    lr_schedule=_optimizer.CosineDecaySchedule(decay_steps=30_000),
+    num_train_steps=30_000,
+    batch_size=192,     # 3 GPUs = 64/GPU (must be divisible by device count)
+    num_workers=16,
+    fsdp_devices=1,
+),

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