Pith Number

pith:KH24IQWZ

pith:2026:KH24IQWZTM6KEMXVP4DHWCFGOR

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When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering

Andrea Bajcsy, Jessie Yuan, Yilin Wu

A robot policy can decide to act, query for clarification, or request human intervention by calibrating its uncertainty estimates with conformal prediction.

arxiv:2602.22474 v2 · 2026-02-25 · cs.RO · cs.LG

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4 Citations 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

We propose uncertainty-aware policy steering (UPS), a framework that jointly reasons about semantic task uncertainty and low-level action feasibility, and selects an uncertainty resolution strategy: execute a high-confidence action, clarify task ambiguity via natural language queries, or ask for action interventions to correct the low-level policy when it is deemed incapable at the task. We leverage conformal prediction to calibrate the composition of the VLM and the pre-trained base policy, providing statistical assurances that the verifier selects the correct strategy.

C2weakest assumption

The assumption that conformal prediction applied to the composition of the VLM verifier and pre-trained policy will yield valid statistical guarantees for strategy selection in practice, and that residual learning from collected interventions will meaningfully improve policy capability without requiring extensive additional data or causing instability.

C3one line summary

UPS framework uses conformal prediction to calibrate VLM verifiers for choosing between high-confidence action execution, natural language task queries, or policy interventions, then applies residual learning from interventions to continually improve the base policy with minimal feedback.

References

100 extracted · 100 resolved · 5 Pith anchors

[1] Let’s think in two steps: Mitigating agreement bias in mllms with self- grounded verification 2026

[2] Con- formal prediction: A gentle introduction.Foundations and trends® in machine learning, 16(4):494–591, 2023 2023

[3] Hallucination of Multimodal Large Language Models: A Survey 2024 · arXiv:2404.18930

[4] Goal inference as inverse planning 2007

[5] $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control 2024 · arXiv:2410.24164

Receipt and verification

First computed	2026-05-18T03:10:03.518995Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

51f5c442d99b3ca232f57f067b08a674720ee37e2d4379f119ba4649d27bac42

Aliases

arxiv: 2602.22474 · arxiv_version: 2602.22474v2 · doi: 10.48550/arxiv.2602.22474 · pith_short_12: KH24IQWZTM6K · pith_short_16: KH24IQWZTM6KEMXV · pith_short_8: KH24IQWZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KH24IQWZTM6KEMXVP4DHWCFGOR \
  | 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: 51f5c442d99b3ca232f57f067b08a674720ee37e2d4379f119ba4649d27bac42

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2a17b09d455fb1e3e82854d98f4d26ba4fdbf72bf557d9d09941097454e0e461",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-02-25T23:23:22Z",
    "title_canon_sha256": "49573dece0fb15d220bb8b69150f477fec2aa11847b1e7c433ebfdb23ffe5a11"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.22474",
    "kind": "arxiv",
    "version": 2
  }
}