axisrobotics/Test-Dataset

AXIS held-out 20 — a cross-embodiment few-shot adaptation benchmark

20 tasks never seen in pretraining, 20 demonstrations each, LoRA adaptation, rollout evaluation.

This release is the SPECIFICATION and the INDEX, not the demonstration data. It is published first and on purpose: everything here is what you need to render the benchmark on your own embodiment, and none of it depends on our video encoding being finished.

Status: the task list is CANDIDATES. The learnability gate (below) has not run yet. It can only remove tasks, never add them, so treat the 20 as an upper bound on the final set.

What is being measured

Few-shot adaptation to a target setup — not generalisation. A policy gets 20 demonstrations of an unseen task, adapts by LoRA, and is evaluated on that same task under the same conditions the demonstrations contain.

That last clause decides a question that is otherwise answered wrongly. Measured from the demonstration data itself:

axis	varies across demos?	so at eval
object identity, placement, jitter	yes	vary
robot initial state	no — 0.22° / 1.8 mm across 856 and 1,148 episodes	fix
camera pose	no	fix
appearance / lighting / texture	no — the randomisation config holds position deltas only	fix

Randomising something at eval that the demos never contain measures a regime the benchmark does not train for, and produces a number that answers no one's question.

Layout

spec/
  benchmark_spec.json    task list, operations, gate thresholds, protocol
  camera_rig.json        the REQUIRED wrist rig, and our measured one (they differ — see below)
  eval_seeds.json        the seed list every embodiment must use
franka/
  index/heldout20_index.jsonl    one row per demonstration

Embodiment-first, so adding an embodiment cannot disturb an existing one.

The camera rig is PRESCRIPTIVE

A cross-embodiment benchmark must fix the rig, or a score difference between embodiments confounds the robot with its cameras.

Wrist camera — reproduce this, expressed relative to your arm's end-effector frame:

<camera mode="fixed" name="eye_in_hand" pos="0.05 0 0" quat="0 0.707108 0.707108 0" fovy="75"/>

Taken verbatim from robosuite/models/assets/robots/panda/robot.xml, which is what LIBERO trains and evaluates against.

Our own Franka renders do NOT comply, and that is why the rig is specified rather than frozen. Derived from the render data by composing the inverse end-effector pose with the recorded camera_to_world, and checked for rigidity (max elementwise std 7.3e-08 — the camera really is bolted on, so the number is a measurement, not a fit):

	required (LIBERO)	measured (our Franka)	delta
hand→camera translation	[0.05, 0, 0] m	[-0.074, 0, 0.0292] m	12.7 cm, opposite side in x
vertical FOV	75°	51.87°	23.1° narrower

Both measured values have zero spread across all 20 tasks. Reproduce the required column.

Third-person camera: one fixed pose shared by every task and episode, [0.95, 0.0, 1.43]. Per episode intrinsics and camera_to_world are in the index.

The index

One row per demonstration. split is adapt (the 20 used for adaptation) or spare.

field	meaning
task_id, attempt_id	the join key. Never join on episode_index — every build renumbers it
source_episode_index	which episode of our render this is
n_frames, fps	15 fps
cameras	per camera intrinsics (3×3 K, row-major) and camera_to_world (4×4, row-major), on adapt rows
camera_names	all 10 rendered cameras: frontview/wrist plus four randomized replicas of each
join_origin	published (18 tasks) or action_match (2) — see below
variant_id, seed, model_id	scene identity

intrinsics is K for the source resolution (640×360). If you render at another resolution, adjust K rather than using it as-is.

Two tasks have a recovered join

Tasks 815 and 1746 ship a task_manifest.json that parses with records: [] while having 1,280 and 40 rendered episodes. Their attempt↔episode pairing was recovered by matching trajectories directly. The method's accuracy was measured, not assumed: scored against task 868's published manifest it was 401/401 correct. Rows carry join_origin so you can tell.

Evaluation protocol

• 50 episodes per task, using spec/eval_seeds.json. Every embodiment draws the same seeds, or a cross-embodiment comparison silently compares different layouts.
• Eval scenes vary object set / placement / jitter only.
• At 50 trials a true 20% rate carries a 95% CI of roughly ±11 points.

The learnability gate (not yet run)

Quality filters say the demonstrations succeed. They say nothing about whether a policy can learn the task from 20 of them — and the previous 10-task version of this benchmark floored, with 4 of 6 evaluated tasks at 0/10. A benchmark whose tasks are all unlearnable ranks nothing.

Every task must satisfy, with pi05_base + 20-demo LoRA:

20% <= success rate <= 80%

Two-sided on purpose. Replacement depth is thin where it matters: pick, place, reach and remove have exactly one eligible task each, so a gate failure there cannot be backfilled.

Known limits, stated rather than discovered

• Robot initial-state robustness is not measured. The demos start from one fixed pose, so the benchmark cannot test it. This is our largest measured weakness elsewhere — on LIBERO-Plus, AXIS pretraining loses 30.9 points on Robot Initial States while gaining 4.5 on Camera Viewpoints and 7.4 on Sensor Noise. That belongs to the pretraining corpus and is fixed by re-rendering that, not by perturbing this benchmark.
• Visual robustness is not measured, for the same reason: no appearance variation exists in the demonstrations.
• pick_place, stack, push, pull are absent — no task reaches the quality bar. A data limit, not an oversight.

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Also in this repository

This repo predates the held-out benchmark and carries other AXIS releases. They are unrelated to the benchmark above and are not part of it:

• stage1_5k_camera_fixed/ — stage-1 5k camera-fixed corpus and its artifacts (AWR weights, schedules, per-task manifests, roots/ranges, norm stats).
• the round-1 stage-2 LIBERO build (2,000 episodes / 338,575 frames / 128 px / LeRobot v3.0).

The held-out benchmark lives entirely under spec/ and <embodiment>/.