| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - manipulation |
| - few-shot |
| - cross-embodiment |
| --- |
| |
| # 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:** |
|
|
| ```xml |
| <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. |
| |
| --- |
| |
| ## 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>/`. |
| |
