Pith Number

pith:J7VTRF22

pith:2026:J7VTRF22R37Z7BDECH37PNVSPY

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How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism

Kana Miyamoto, Kanata Suzuki, Tetsuya Ogata

Failure demonstrations can improve imitation learning policies when selected by measuring attention discrepancies between successes and failures.

arxiv:2605.07560 v2 · 2026-05-08 · cs.RO

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\pithnumber{J7VTRF22R37Z7BDECH37PNVSPY}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Simulation results show that the proposed method improves task success rates when trained with failure data and that the proposed metric identifies failure samples that are beneficial for learning when combined with successful demonstrations.

C2weakest assumption

That the post-training attention discrepancy metric reliably identifies failure samples that improve rather than degrade policy performance, without introducing selection bias or requiring task-specific tuning.

C3one line summary

The method uses attention discrepancy metrics on latent success-failure representations to select beneficial failure data for imitation learning, raising task success rates in simulations.

Receipt and verification

First computed	2026-05-21T02:05:04.542495Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4feb38975a8eff9f846411f7f7b6b27e02a57482d66c2cb8bc1c58b609705c6a

Aliases

arxiv: 2605.07560 · arxiv_version: 2605.07560v2 · doi: 10.48550/arxiv.2605.07560 · pith_short_12: J7VTRF22R37Z · pith_short_16: J7VTRF22R37Z7BDE · pith_short_8: J7VTRF22

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/J7VTRF22R37Z7BDECH37PNVSPY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4feb38975a8eff9f846411f7f7b6b27e02a57482d66c2cb8bc1c58b609705c6a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "cb6ebc42dd0c48c9aec5091113e9fbfca0f1c62d25fbd9ca9049f5864446a889",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-08T10:34:11Z",
    "title_canon_sha256": "6a8ce84b86362d791902dcdc5c0c64803da0c3d18357d657427dcaf5c73eb71c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.07560",
    "kind": "arxiv",
    "version": 2
  }
}