gr00t-n1.6-aloha-kitchen-task-12

GR00T N1.6-3B — ALOHA "Kitchen Task 1 + Task 2" (two-task mixture), 25 Hz / H=50, 80K steps

Two-task fine-tune on a real stationary ALOHA, trained on the union of the Kitchen_Task_1 and Kitchen_Task_2 datasets. Siblings: gr00t-n1.6-aloha-kitchen-task-1 and gr00t-n1.6-aloha-kitchen-task-2, the same two tasks trained separately with the same recipe. No task-success evaluation has been run on this checkpoint — this card makes no success claim.

• Robot: stationary ALOHA (aloha_stationary), bimanual, 14-dim state/action: right_arm 0:6, right_gripper 6:7, left_arm 7:13, left_gripper 13:14.
• Arms are RELATIVE (delta w.r.t. the current joint state), grippers ABSOLUTE: pass the current 14-dim state with every call.
• State dropout 0.3 during training. config.json in every published checkpoint has state_dropout_prob set to 0.0 for inference (the action_head.mask_token weight is then an ignored key).

Data — two datasets, equal sampling, union normalization

Both are LeRobot v2.1, 4 cameras (high, low, wrist_left, wrist_right) at 480x640 h264, 1.5 GB each, and their meta/modality.json files are byte-identical (sha256 46507a200698d881f086240cc1caf308c4490fa8d8a0d09f3ecd887a4cd58563), so the single modality.json published at the repo root applies to both. No held-out split — all 101 episodes are trained on.

Two properties of the mixture that a downstream user should know, both measured, not assumed:

• Sampling is equal (50/50), not length-proportional. The run was launched with mix ratios 1 1, which the loader normalizes to weights [0.5, 0.5]. The two datasets shard into 56 shards / 57,189 samples (Kitchen_Task_1) and 53 shards / 53,984 samples (Kitchen_Task_2), so length-proportional sampling would have been [0.5144, 0.4856] — the difference from 50/50 is 1.44 pp, small here but a real design choice rather than a coincidence.
• Normalization statistics are the union of both datasets. The published statistics.json is the merge of the two per-dataset statistics at those same [0.5, 0.5] weights, i.e. the equal-weight average of the per-dataset means for every joint group (e.g. state.left_arm mean[0] = (-0.09445 + 0.04231)/2 = -0.02607, action.right_gripper = (0.72607 + 0.79685)/2 = 0.76146). This was confirmed by comparing the in-run checkpoint-15000/statistics.json against an independent offline recomputation of the merge: all 8 joint groups matched digit for digit. Consequence: this model's normalization differs from both single-task repos, so the three checkpoints are not drop-in interchangeable behind one normalization layer.

Recipe

• Model-only keep copies every 10k steps; DeepSpeed 0.17.5, save_steps 5,000. Final train_loss 0.015039, train_runtime 51,890.1 s (1.542 steps/s), epoch 1.0. Inference files only are published (no optimizer / scheduler / trainer state / RNG state).
• Published checkpoints: 10k, 20k, 30k, 40k, 50k, 60k, 70k, 80k under r25_h50/checkpoint-<step>/.
• Apart from the dataset list and the step count, the recipe is the same as the two single-task repos.

Training lineage

One Slurm job, no resume. gpu26 job 15764 bo_kitchen12_r25_h50_80k ran 0 -> 80,000 steps in a single attempt (14 h 29 m on 4 GPUs) and COMPLETED; trainer_state.json of the final checkpoint reads global_step 80000. There was no restart, no mid-run recipe or loader change, and no checkpoint in this repo was produced under different settings from any other.

Deploy note

• Run the policy at 25 Hz control. Each call returns a 50-step action chunk = 2.0 s of motion.
• Arm entries are relative joint targets — add them to the current measured joint positions (use_relative_action-style); gripper entries are absolute.
• The model is language-conditioned over the two task strings above; it has not been checked for generalization to unseen instructions.

Layout: r25_h50/checkpoint-<step>/ (+ r25_h50/aloha_config.py, the modality config the run actually trained with), modality.json (the shared Kitchen_Task_1 / Kitchen_Task_2 meta/modality.json), publication_manifest.json (per-file sizes + sha256 of everything published).

Provenance: gpu26 Slurm job 15764 bo_kitchen12_r25_h50_80k, COMPLETED 2026-09-16 05:39:24 UTC (14 h 29 m, 4 GPUs).