task5 setting_mousetraps, human demos only, step 21000 (params only)

.gitattributes

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README.md

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+---
+license: apache-2.0
+tags:
+  - robotics
+  - behavior-1k
+base_model: IliaLarchenko/behavior_50t_checkpoint
+---
+
+# b1k task-5 checkpoints (`setting_mousetraps`)
+
+Params-only checkpoints for offline evaluation. `train_state` is **not** included, so these
+cannot be resumed from — inference/eval only.
+
+## `task5_human_only_21k`
+
+Single-task fine-tune of the 50-task meta checkpoint on **task 5 = `setting_mousetraps`**,
+using the **200 human demonstrations** from the 2026 BEHAVIOR-1K challenge set and nothing else.
+
+| | |
+|---|---|
+| init | `IliaLarchenko/behavior_50t_checkpoint` (params) |
+| data | `2026-challenge-demos-224`, activity `setting_mousetraps`, 200 episodes / 2,039,222 frames |
+| step | 21,000 of a planned 30,000 (run stopped early; resumable copy retained locally) |
+| batch | 224 (32/GPU × 7× H200), FSDP over 7 devices |
+| LR | 8.75e-7 → 8.75e-5 → 1.75e-6, 2,000-step warmup (linear rule off BS-128 / 5e-5) |
+| norm stats | `norm-stats-fixed` (sha256 `2b42bdfb…`), i.e. 2025 stats with the robot-frame `base_qvel` correction on state dims 0–2 |
+| task space | `B1K_TASK_SPACE=100`; `setting_mousetraps` is index 5 in both the 2025 and 2026 tables, so its task/stage embeddings are **pretrained**, not randomly initialised |
+| final logged | `action_loss = 0.0209` at step 21,775 |
+
+### Reading the loss
+
+`action_loss` here is **training-set** loss. There is no validation split and no rollout
+evaluation has been run, so it says nothing about task success or generalisation. It is also
+not comparable across tasks — episode length varies ~6× between activities.
+
+### Reproducing the input pipeline
+
+The model consumes a 23-dim state extracted from the 61-dim `observation.state`
+(`base_qvel` 0:3, `arm_left` 3:10, `gripper_left` 24:26, `arm_right` 28:35,
+`gripper_right` 49:51, `trunk` 53:57), and predicts 23-dim actions with the trunk and both
+arms as **deltas relative to the current state** (`use_delta_joint_actions=True`); base
+velocity and both grippers stay absolute. Action normalisation is **per-timestamp**
+(`use_per_timestamp_norm=True`) over a 30-step horizon — the scalar `mean`/`std` in
+`norm_stats.json` are not what the pipeline divides by for actions.
+
+Cameras are 224×224 h264: `zed_link_camera_0` (head), `left_realsense_link_camera_0`,
+`right_realsense_link_camera_0`.

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task5_human_only_21k/params/array_metadatas/process_0

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