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.

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).

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).

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.

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).

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.

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.

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.