README.md

metadata

license: cc-by-4.0
pretty_name: RoboMME-Interference
size_categories:
  - 10K<n<100K
tags:
  - robotics
  - vision-language-action
  - memory
  - benchmark
  - evaluation
  - cross-session
configs:
  - config_name: default
    data_files:
      - split: test
        path: canonical_rollouts.csv
  - config_name: retrieval
    data_files:
      - split: test
        path: retrieval_rollouts.csv
  - config_name: success_rates
    data_files:
      - split: test
        path: tables/main_success_rates.csv

RoboMME-Interference

Every rollout from RoboMME-Interference, a cross-session benchmark that measures whether a memory-augmented robot policy can still use a relevant past session once unrelated sessions accumulate in its history.

The benchmark is built on RoboMME (Dai et al., ICML 2026), reusing its tasks, memory-augmented π₀.₅ variants, and released checkpoints. This dataset is the evaluation output, not robot data: one row per rollout, with the outcome and the composition of the history buffer the policy was given.

• Paper: arXiv:2606.22338
• Project page: https://robotmemorybench.com
• Code: https://github.com/SoumilRathi/robomme-interference

The protocol

For each query episode, the policy is given an external history buffer holding the episode's relevant prior demonstration (the lesson), followed by k unrelated distractor sessions from other task families. Larger k pushes the lesson farther back.

condition	History buffer contents
no-history	empty
k0	the lesson only
k1, k3, k7	the lesson, then 1, 3, or 7 unrelated sessions

Eight memory systems, each across nine task families, five conditions, and 50 test episodes, gives 18,000 rollouts. The π₀.₅ baseline has no memory and appears only at no-history, adding 450, for 18,450 rollouts over 369 cells.

Scoring convention

Success rate is successes / 50 per cell. Episodes that hit the step limit have status == "ongoing" and count as non-successes: they stay in the denominator. Filtering them out inflates every rate.

from datasets import load_dataset

ds = load_dataset("SoumilR/robomme-interference", split="test")
cell = ds.filter(lambda r: r["variant"] == "perceptual-framesamp-modul"
                       and r["family"] == "MoveCube" and r["condition"] == "k7")
rate = sum(r["status"] == "success" for r in cell) / len(cell)  # 0.32

Configs

Config	Rows	Contents
default	18,450	every rollout across all nine systems (canonical_rollouts.csv)
retrieval	1,800	rollouts for Retrieval-FrameSamp-Modul (retrieval_rollouts.csv)
success_rates	41	per-system, per-condition success rates with Wilson 95% intervals

tables/ also holds success rates per family and per difficulty, each system's lift over its own no-history rate, and the k0-to-k7 drops.

Columns

Shared by both rollout files:

Column	Description
family	task family: MoveCube, RouteStick, VideoUnmask, VideoUnmaskSwap, VideoRepick, VideoPlaceButton, VideoPlaceOrder, InsertPeg, PatternLock
condition	no-history, k0, k1, k3, k7
episode	test-episode index, 0–49; the same identity across conditions, so comparisons are paired
seed	simulator seed
difficulty	RoboMME's easy / medium / hard stratification
status	success, fail, or ongoing (hit the step limit)
success	boolean, equivalent to status == "success"
steps	environment steps taken

canonical_rollouts.csv adds:

Column	Description
variant	one of perceptual-{framesamp,tokendrop}-{modul,context,expert}, recurrent-ttt-{context,expert}, pi05_baseline
distractors	number of unrelated sessions in the buffer
buffer_frames_start, buffer_frames_end	buffer size in frames, before and after the rollout
actual_*_lesson,filler,query and *_fraction	how much of the buffer came from the lesson, the distractors, and the live query episode, at rollout start and end
timing and cache columns	wall-clock and server-side latency per phase, feature-cache hit counts

retrieval_rollouts.csv adds:

Column	Description
retrieval_model	encoder and segmentation setting used for retrieval
retrieval_selected_{lesson,filler,query}_fraction	share of the retrieved segment that came from the lesson, the distractors, and the query episode

The mean retrieval_selected_lesson_fraction is 0.96, so what retrieval delivers to the policy is almost entirely the lesson.

Variant names

Paper names map to variant values as FrameSamp → framesamp, TokenDrop → tokendrop, Recurrent-TTT → recurrent-ttt, and the integration suffixes -Modul, -Context, -Expert → -modul, -context, -expert.

Citation

@article{rathi2026robomme_interference,
  title  = {RoboMME-Interference: Benchmarking Robot Memory Under Interference},
  author = {Rathi, Soumil},
  journal = {arXiv preprint arXiv:2606.22338},
  year   = {2026}
}

Please also cite RoboMME, which this benchmark builds on.

License

CC BY 4.0. The simulator checkpoints and raw rollout videos are not redistributed here; they come from RoboMME.