Pith Number

pith:QKJPCUZF

pith:2025:QKJPCUZF4W5AE62HTEJ6C5DBZT

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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Systems

Andreea Bobu, David Lee, Jordan Abi Nader, Nathaniel Dennler

QuickLAP fuses language feedback as probabilistic observations with physical corrections to infer robot reward functions in real time.

arxiv:2511.17855 v2 · 2025-11-22 · cs.AI · cs.RO

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

QuickLAP reduces reward learning error by over 70% compared to physical-only and heuristic multimodal baselines in a semi-autonomous driving simulator, with a 15-participant user study showing significantly higher understandability, collaboration, and preference for the learned behavior.

C2weakest assumption

That large language models can reliably extract accurate reward feature attention masks and preference shifts from free-form user utterances without introducing systematic bias or hallucination that would degrade the Bayesian fusion.

C3one line summary

QuickLAP fuses language and physical feedback in a Bayesian update to learn reward functions in real time for semi-autonomous systems, reducing error by over 70% versus physical-only and heuristic baselines.

References

70 extracted · 70 resolved · 0 Pith anchors

[1] In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML) 2004 · doi:10.1145/1015330.1015430

[2] Henny Admoni and Brian Scassellati. 2017. Social eye gaze in human-robot interaction: a review.Journal of Human-Robot Interaction6, 1 (2017), 25–63 2017

[3] Andrea Bajcsy, Dylan P. Losey, Marcia K. O’Malley, and Anca D. Dragan. 2018. Learning from Physical Human Corrections, One Feature at a Time. InPro- ceedings of the 2018 ACM/IEEE International Confere 2018 · doi:10.1145/3171221.3171267

[4] Andrea Bajcsy, Dylan P. Losey, Marcia K. O’Malley, and Anca D. Dragan. 2017. Learning Robot Objectives from Physical Human Interaction. InProceedings of the 1st Annual Conference on Robot Learning (Pr 2017

[5] Chris L Baker, Joshua B Tenenbaum, and Rebecca R Saxe. 2007. Goal inference as inverse planning. InProceedings of the Annual Meeting of the Cognitive Science Society, Vol. 29 2007

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:33.011687Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8292f15325e5ba027b479913e17461ccc6d2f04bec79f5d85225a876233d2055

Aliases

arxiv: 2511.17855 · arxiv_version: 2511.17855v2 · doi: 10.48550/arxiv.2511.17855 · pith_short_12: QKJPCUZF4W5A · pith_short_16: QKJPCUZF4W5AE62H · pith_short_8: QKJPCUZF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QKJPCUZF4W5AE62HTEJ6C5DBZT \
  | 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: 8292f15325e5ba027b479913e17461ccc6d2f04bec79f5d85225a876233d2055

Canonical record JSON

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    "cross_cats_sorted": [
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-11-22T00:45:33Z",
    "title_canon_sha256": "f54530b9854b1d970c515e608ea7372d2c911ad1e3fc75365d6917fff23f56e3"
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