README.md

---
license: apache-2.0
tags:
  - robotics
  - behavior-1k
base_model: IliaLarchenko/behavior_50t_checkpoint
---

# b1k single-task checkpoints

Params-only checkpoints for offline evaluation. `train_state` is **not** included, so these
cannot be resumed from — inference/eval only.

<!-- task1-begin -->
## `task1/` — picking_up_trash, human demos + recovery data

Single-task fine-tune of the 2025 winner's **`checkpoint_2`** (the official checkpoint covering
task 1) on **task 1 = `picking_up_trash`**, using the 200 human demos plus 788 scripted recovery
episodes from `fastwalker1118/B1k_Recovery` (`task1/Demo+Recovery`). Planned 5 epochs; these are
the checkpoints nearest the epoch boundaries (one epoch = 15,089 steps).

| checkpoint | step | epochs | `action_loss` at that step (train set) |
|---|---|---|---|
| [`task1/task1_demo_recovery_epoch1_15000`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task1/task1_demo_recovery_epoch1_15000) | 15,000 | 0.99 | 0.0635 |
| [`task1/task1_demo_recovery_epoch2_30000`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task1/task1_demo_recovery_epoch2_30000) | 30,000 | 1.99 | 0.0461 |
| [`task1/task1_demo_recovery_epoch3_45000`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task1/task1_demo_recovery_epoch3_45000) | 45,000 | 2.98 | 0.0368 |

| | |
|---|---|
| init | `IliaLarchenko/behavior_submission` `checkpoint_2` (params) |
| config | `pi_behavior_b1k_fast` (FSDP 4, EMA, FAST aux loss), `B1K_TASK_SPACE=100` |
| data | 988 episodes (200 demos + 788 recoveries), 1,931,447 samples per epoch; source videos (head 720², wrists 480²) re-encoded to 224×224 with lanczos + the upstream x265 option string (crf 28, keyint 8), frame alignment verified |
| batch | 128 on 4× H200 |
| LR | 2.5e-7 → 2.5e-6 at step 500 → cosine to 2.5e-7 at step 75,445 (5 epochs) |
| norm stats | 2025 stats + robot-frame `base_qvel` fix (state dims 0–2) **+ `trunk_3` un-frozen**: action dim 6 / state trunk_3 mean 0, std 0.1315, measured on the recoveries (the 2025 stats have std ≈ 0 because the demos never rotate the waist; the recoveries do) |
| targets | normalized flow-matching action targets clipped to ±10σ (scripted-planner recoveries have rare far-out chunk starts) |

> **Eval: load the norm stats shipped in each checkpoint's `assets/`**, not the 2025
> `checkpoint_2` stats. With the 2025 stats the waist (`trunk_3`) input and output are mis-scaled.

`action_loss` is training-set loss only (no validation split, no rollouts yet).
<!-- task1-end -->

<!-- task2-begin -->
## `task2/` — putting_away_Halloween_decorations, human demos + recovery data (round 0)

Single-task fine-tune of the 2025 winner's **`checkpoint_1`** (the official checkpoint covering task 2) on
**task 2 = `putting_away_Halloween_decorations`**, using the 200 human demos plus 543 scripted recovery episodes
from `fastwalker1118/B1k_Recovery` (`task2/Demo+Recovery_video_r0a`). The recoveries start from failure frames
that a video-only inspection found in `checkpoint_1` rollouts, and from edited variants of those frames
(no public-test instances). 2 epochs; one epoch =
31,262 steps.

| checkpoint | step | epochs | `action_loss` near that step (train set) |
|---|---|---|---|
| [`task2/task2_demo_recovery_video_r0a_epoch1_30000`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task2/task2_demo_recovery_video_r0a_epoch1_30000) | 30,000 | 0.96 | 0.0476 |
| [`task2/task2_demo_recovery_video_r0a_epoch2_62523`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task2/task2_demo_recovery_video_r0a_epoch2_62523) | 62,523 | 2.00 | 0.0372 |

| | |
|---|---|
| init | `IliaLarchenko/behavior_submission` `checkpoint_1` (params) |
| config | `pi_behavior_b1k_fast` (FSDP 4, EMA, FAST aux loss), `B1K_TASK_SPACE=100` |
| data | 743 episodes (200 demos + 543 recoveries), 4,001,658 samples per epoch; all videos re-encoded to 224×224 with lanczos + the full upstream x265 option string (crf 28, keyint 8) |
| batch | 128 on 4× H200 |
| LR | 2.5e-7 → 2.5e-6 at step 500 → cosine to 2.5e-7 at step 62,524 (2 epochs) |
| norm stats | 2025 `checkpoint_1` stats + robot-frame `base_qvel` fix (state dims 0–2); `trunk_3` stays frozen (the task-2 recoveries never rotate the waist) |
| targets | normalized flow-matching action targets clipped to ±10σ |

> **Eval: robot-frame base velocity in the state, and the norm stats shipped in each checkpoint's `assets/`.**

`action_loss` is training-set loss only (no validation split).
<!-- task2-end -->

<!-- task2-srcmeta-begin -->
## `task2/…_srcmeta_…` — same round-0 fine-tune, with data-source metadata (pi0.7-style)

Identical to the `task2/` round-0 fine-tune above (same data, init, recipe, seed, norm stats) except that every
training example carries its **data source as prompt metadata**, following π0.7 (arXiv 2604.15483, Sec. V-C/V-E):
`Data source: human teleoperation.` for the 200 human demos, `Data source: scripted motion planner.` for the 543
recovery episodes. The label is 7 Gemma text tokens embedded with the VLM's own token-embedding table and appended
to the prefix after the state tokens as a causal block (the image/task/stage/state tokens do not attend to it; the
action expert and FAST tokens do); no parameters are added. As in π0.7 the metadata is dropped entirely 15% of the
time and per component 5% of the time. **At inference set the label** (we evaluate `human`; `planner` for comparison)
— the patch is [`task2/source_meta.py`](https://huggingface.co/fastwalker1118/b1kcheckpoints/blob/main/task2/source_meta.py); unpatched code runs these weights with no label block at all.
Recoveries come from `fastwalker1118/B1k_Recovery` (`task2/Demo+Recovery_video_r0a`). The recoveries start from failure frames
that a video-only inspection found in `checkpoint_1` rollouts, and from edited variants of those frames
(no public-test instances). 2 epochs; one epoch =
31,262 steps.

| checkpoint | step | epochs | `action_loss` near that step (train set) |
|---|---|---|---|
| [`task2/task2_demo_recovery_video_r0a_srcmeta_epoch1_30000`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task2/task2_demo_recovery_video_r0a_srcmeta_epoch1_30000) | 30,000 | 0.96 | 0.0476 |
| [`task2/task2_demo_recovery_video_r0a_srcmeta_epoch2_62523`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task2/task2_demo_recovery_video_r0a_srcmeta_epoch2_62523) | 62,523 | 2.00 | 0.0358 |

| | |
|---|---|
| init | `IliaLarchenko/behavior_submission` `checkpoint_1` (params) |
| config | `pi_behavior_b1k_fast` (FSDP 4, EMA, FAST aux loss), `B1K_TASK_SPACE=100` |
| data | 743 episodes (200 demos + 543 recoveries), 4,001,658 samples per epoch; all videos re-encoded to 224×224 with lanczos + the full upstream x265 option string (crf 28, keyint 8) |
| batch | 128 on 4× H200 |
| LR | 2.5e-7 → 2.5e-6 at step 500 → cosine to 2.5e-7 at step 62,524 (2 epochs) |
| norm stats | 2025 `checkpoint_1` stats + robot-frame `base_qvel` fix (state dims 0–2); `trunk_3` stays frozen (the task-2 recoveries never rotate the waist) |
| targets | normalized flow-matching action targets clipped to ±10σ |

> **Eval: robot-frame base velocity in the state, and the norm stats shipped in each checkpoint's `assets/`.**

`action_loss` is training-set loss only (no validation split).
<!-- task2-srcmeta-end -->

<!-- task2-human-begin -->
## `task2/task2_demo_only_…` — human-demos-only baseline (no recoveries)

Control for the `task2/` round-0 recovery fine-tunes above: the same init, recipe, seed and norm stats, trained for
**one epoch on the 200 task-2 human demos only** (the demo half of `task2/Demo+Recovery_video_r0a`, read from the
same 224×224 files). It measures how much of the round-0 gain comes from the recoveries rather than from
fine-tuning `checkpoint_1` on the 2026 demos.

| checkpoint | step | epochs | `action_loss` near that step (train set) |
|---|---|---|---|
| [`task2/task2_demo_only_ckpt1_1epoch_21495`](https://huggingface.co/fastwalker1118/b1kcheckpoints/tree/main/task2/task2_demo_only_ckpt1_1epoch_21495) | 21,495 | 1.00 | 0.0555 |

| | |
|---|---|
| init | `IliaLarchenko/behavior_submission` `checkpoint_1` (params) |
| config | `pi_behavior_b1k_fast` (FSDP 4, EMA, FAST aux loss), `B1K_TASK_SPACE=100` |
| data | 200 human demos, 2,751,612 samples per epoch |
| batch | 128 on 4× H200 |
| LR | 2.5e-7 → 2.5e-6 at step 500 → cosine to 2.5e-7 at step 21,496 (1 epoch) |
| norm stats | 2025 `checkpoint_1` stats + robot-frame `base_qvel` fix (state dims 0–2) |

> **Eval: robot-frame base velocity in the state, and the norm stats shipped in each checkpoint's `assets/`.**

`action_loss` is training-set loss only (no validation split).
<!-- task2-human-end -->

## `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 |

## `task10_taskfinetune`

> **Rollout evaluation:** see [`task10_taskfinetune/EVAL_RESULTS.md`](task10_taskfinetune/EVAL_RESULTS.md) —
> 0.0 % success / avg q 0.1917 over 20 `public_test` instances, vs 5.0 % / 0.1750 for the
> official `checkpoint_1`. The difference is not statistically significant (sign test p = 1.000).

Single-task fine-tune of the 50-task meta checkpoint on **task 10 =
`set_up_a_coffee_station_in_your_kitchen`**, using the **200 human demonstrations** from the
2026 BEHAVIOR-1K challenge set and nothing else. Run completed in full.

| | |
|---|---|
| init | `IliaLarchenko/behavior_50t_checkpoint` (params) |
| data | `2026-challenge-demos-224`, activity `set_up_a_coffee_station_in_your_kitchen`, 200 episodes / 1,253,243 frames |
| step | 29,999 of 30,000 (**completed**, 16 h 54 m on 8× H200) |
| batch | 256 (32/GPU × 8× H200), FSDP over 8 devices |
| LR | **flat 5e-6** — `init = peak = decay = 5e-6`, so no warmup ramp and no cosine decay |
| norm stats | 2025 stats with the robot-frame `base_qvel` correction on state dims 0–2 (same `qvelfix` stats as the task-5 runs) |
| task space | `B1K_TASK_SPACE=100`; `set_up_a_coffee_station_in_your_kitchen` is index 10 in both the 2025 and 2026 tables, so its task/stage embeddings are **pretrained**, not randomly initialised |
| final logged | `action_loss = 0.0294`, `total_loss = 0.0565`, `fast_accuracy = 0.8208` at step 29,975 |

Because the LR is flat with no annealing, the final step is **not** necessarily the best
checkpoint — there is no decay phase to settle into a minimum. Steps 4000/8000/.../28000 were
retained locally and can be uploaded if you want to sweep across them.

### Video encoding caveat for this checkpoint

Trained on the `2026-challenge-demos-224` build, whose bitstream reports **x264 `crf=20.0`,
`keyint=250`**. Measured against `b1k-224x224-gop8-fixed` (x265 `crf=28.0`, `keyint=8`) on
identical frames, this build is slightly softer: **0.886× Laplacian variance, 0.933× HF
spectral energy**. The downscaling filter is not recorded in the bitstream and is unverified.
No task-success delta has been measured for this difference — it is an input-statistics
observation only.

One video file in the source data (`right_realsense .../chunk-010/file-002.mp4`, backing 46 of
the 200 episodes) had no moov atom and was unreadable; it was re-encoded from the intact
480×480 HEVC original at x264 CRF 23 / GOP 250 to match its neighbours, verified at 43–50 dB
PSNR against the source.

### 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`.