fastwalker1118/b1kcheckpoints

b1k single-task checkpoints

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

task10_taskfinetune

Rollout evaluation: see 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.