Pith. sign in

REVIEW 3 cited by

Grounding Classical Task Planners via Vision-Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2304.08587 v3 pith:73CL5H45 submitted 2023-04-17 cs.RO

Xiaohan Zhang , Yan Ding , Saeid Amiri , Hao Yang , Andy Kaminski , Chad Esselink , Shiqi Zhang This is my paper · ORCID
classification cs.RO
keywords actionclassicalchallengesmodelsplannersplanningtasktpvqa
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due to the strong assumptions of perfect perception and action executions. To tackle these challenges, one solution is to connect the symbolic states and actions generated by classical planners to the robot's sensory observations, thus closing the perception-action loop. This research proposes a visually-grounded planning framework, named TPVQA, which leverages Vision-Language Models (VLMs) to detect action failures and verify action affordances towards enabling successful plan execution. Results from quantitative experiments show that TPVQA surpasses competitive baselines from previous studies in task completion rate.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tactile Modality Fusion for Vision-Language-Action Models

    cs.RO 2026-03 conditional novelty 6.0 of 10

    A FiLM-based tactile fusion method that conditions VLA visual features on frozen pretrained touch embeddings improves real-robot insertion success, speed, and force control relative to vision-only and concatenation baselines.

  2. Augmented Vision-Language Models: A Systematic Review

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A structured taxonomy of inference-time augmentation techniques that connect vision-language models to external symbolic systems, tools, and knowledge sources.

  3. VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots

    cs.RO 2025-07 reject novelty 4.0 of 10

    An LLM plus LTL-based verification module that reorders, inserts, and removes steps in household robot plans, reporting reduced ordering errors but with weak experimental support.

Pith tools