gr00t-n1.7-g1-dex1-ikea-3task-46d-30hz-h40

IKEA three-task, 46-dim state — best at pick table leg without arm velocity

GR00T N1.7 fine-tuned on carroll511/IKEA_table_assembly — a Unitree G1 with Dex1 grippers assembling a children's table from a fixed standing position.

g1_dex1_ikea_relarm_3view_aug_b64_v2 / checkpoint-20000.

Why this checkpoint

Of every model trained here without arm joint velocities, this one has the lowest wrist error on pick table leg over the window deployment actually executes:

All four are scored on the same 180 windows, so the numbers line up. The three-task model wins because it is not paying for the two tasks the five-task models also carry: adding turn the tabletop square and flip table costs the original tasks about 12% on insert, and pick pays a smaller share of the same bill.

A 46-dim model is 15-20% behind its 60-dim counterpart. Arm joint velocity has replicated five times now; if the deployment can supply left_arm_vel/right_arm_vel, use a 60-dim model instead. This one exists for the case where it cannot.

What it does and does not do

Three instructions only:

1. pick table leg
2. insert table leg to table base
3. rotate leg to tighten

No turn the tabletop square, no flip table. Feeding it either string is undefined — it was never trained on them.

Setup

Open-loop accuracy

Held-out _v2_val split, stride 10, 4 denoising steps, fixed per-window seed.

Read the 1-8 step figures: async deployment executes about 8 steps after latency compensation, so late chunk steps never run. eval_loss does not track action quality on this task — it rises while open-loop accuracy improves, so checkpoints are selected by this scan and never by the loss.

Scored on the v2 val split. Not comparable to v1-era numbers — the split differs, so the absolute values differ (v1 baseline EE8 10.66 mm against v2's 11.86 mm for the same recipe). Compare only within this split.

Caveats

• Open-loop only; closed-loop behaviour on hardware unverified for this checkpoint.
• Arms are RELATIVE, grippers ABSOLUTE; get_action() returns unnormalized absolute values.
• The waist is not commanded; recordings are from a fixed stance (waist pitch bimodal at ~2.6/10.4 deg — set the robot near 10.5 deg, the majority mode).
• Trained on 178 episodes / 79,205 frames. A later recording roughly doubles the insert data; models trained on that are separate.