RoboPro-Baselines

Evaluation of three base (non-DA3) VLA policies — PI0.5, PI0 and X-VLA — on the RoboPRO benchmark. All metrics are percentages, micro-averaged over episodes.

SR = success rate · HSR = hard success rate (success and no collision) · CR = collision rate

What's in here

Study	Scope	Where
Full perturbation sweep	13 axes x 12 tasks x clean+clutter, all 3 models (5,148 cells)	perturbation_full/
Baseline task suite	79 tasks x 4 scenes, clean and clutter, all 3 models	<model>/clean/, <model>/clutter/
PI0.5 data composition	100 demos/task split 3 ways + default baseline	pi05/data_variants/
Half-subset baselines	50 clean + 50 clutter per task, 12 tasks	half_models/
Collision breakdown	floor vs object collision rates	collision_breakdown/
Checkpoints	trained model weights	pi05-roboreal-half-20k/, pi0-roboreal-half-40k/, xvla-roboreal-half-60k/

Headline: robustness under perturbation (12 tasks)

Non-perturbed baseline vs the full 13-axis perturbation sweep, on the same 12 tasks.

Model	Baseline clean	Perturbed clean	Baseline clutter	Perturbed clutter
PI0.5	78.8	74.7	51.3	52.3
PI0	65.4	56.5	34.6	41.0
X-VLA	51.7	44.9	30.4	32.5

SR shown. PI0.5 > PI0 > X-VLA holds on every axis and in both splits. Perturbation costs each model 4-9 SR points on clean, as expected.

The clutter columns behave differently and should be read with care: perturbed clutter scores higher than baseline clutter for all three models (PI0.5 51.3 -> 52.3, PI0 34.6 -> 41.0, X-VLA 30.4 -> 32.5). Perturbation does not make tasks easier. The two campaigns build cluttered scenes from different configs — the baseline uses bench_demo_<scene>_<density> while the perturbation sweep uses bench_perturb_* / bench_verified_* — so their clutter is not the same distribution and the clutter columns are not directly comparable across campaigns. Within a single campaign the comparisons are sound; the clean columns, which share a configuration, are the ones to compare across campaigns.

Perturbation by axis group (SR, clean / clutter)

Note on X-VLA Object-OOD: the campaign was concluded with 79 X-VLA object-OOD cells unevaluated, so that one cell of the table below is computed over a task subset and is not matched to the other two models. Every other figure on this page is complete.

Axis group	PI0.5	PI0	X-VLA
Language (3 axes)	68.3 / 46.5	51.2 / 36.9	38.8 / 28.6
Distractor (2)	70.2 / 46.5	53.5 / 33.8	35.6 / 28.8
Vision (4)	78.6 / 58.5	57.3 / 47.0	52.1 / 35.2
Object-OOD (4)	78.3 / 53.4	61.4 / 41.7	47.3 / 35.4

Language and distractor perturbations hurt most, vision least — the same ordering for all three models, pointing at instruction grounding rather than visual robustness as the weak point.

Baseline task suite (79 tasks, per model)

Model	Clean SR / HSR / CR	Clutter SR / HSR / CR (avg d6-d15)
PI0.5	70.2 / 65.7 / 11.9	60.9 / 47.8 / 31.7
PI0	60.1 / 55.9 / 14.7	47.3 / 32.9 / 44.6
X-VLA	49.5 / 46.7 / 9.4	39.6 / 27.5 / 38.7

Per-scene and per-task breakdowns are in <model>/clean/<model>_clean.csv and <model>/clutter/<model>_clutter.csv.

PI0.5 training-data composition (12 tasks)

Training data	Demos/task	Clean SR	Clutter SR
default — full dataset	full	78.8	51.3
50 clean + 50 clutter	100	74.2	62.9
100 clean only	100	71.2	23.8
100 clutter only	100	66.7	55.4

A 50/50 mix beats the full-dataset default on clutter by +11.6 SR using only 100 demos per task. Clean-only training collapses in clutter (23.8 SR, 79.2 CR). Full discussion in pi05/data_variants/.

Directory layout

perturbation_full/                     # full 13-axis sweep: metrics, cell audit, methodology
<model>/clean/<model>_clean.csv        # 79-task baseline, obstacle-free
<model>/clutter/<model>_clutter.csv    # 79-task baseline, densities d6-d15 averaged
<model>/perturbation/                  # per-model copy of the 13-axis sweep
    <model>_perturbation.csv           #   per axis/scene/task/condition
    <model>_perturbation_averages.csv  #   scene-level aggregates
<model>/<group>/<scene>/<task>/
    _episodes.jsonl                    # per-seed rows, each tagged with its config
    <config>_seed<seed>.mp4            # rollout video
pi05/data_variants/                    # training-data composition study

Reading the numbers

Use HSR, not SR, when comparing under clutter. All three models lose ~12 SR points when collisions are counted as failures (X-VLA 12.1, PI0 12.6, PI0.5 12.0), with collision rates of 48-53%. Roughly a quarter of every model's cluttered 'successes' involve pushing through an obstacle, and no model is meaningfully better at avoidance than the others — PI0.5 leads by succeeding more often, not more gently.

A small number of cells are incomplete and are excluded from all averages above; 27 of 5,148 cells (0.52%) are permanently short by one rollout for documented hardware reasons. See perturbation_full/README.md and accepted_gaps.json for the per-cell record.