AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 9 Sep 2026] Title:Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints View a PDF of the paper titled Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints, by Qinzhen Ma (Rice University) and 1 other authors View PDF HTML (experimental) Abstract:Accurate contact prediction is useful for robotic manipulation only if it supports effective decisions. We investigate this connection using a compact, randomly initialized visuotactile world model, trajectory-level uncertainty calibration, and behavior-initialized actor-critic learning in imagination. On 160 MuJoCo Lift episodes, adding touch reduces endpoint-force prediction error from 1.058 to 0.228 N and interval-peak error from 2.724 to 0.523 N across three training seeds. However, tactile persistence achieves lower errors of 0.095 and 0.498 N, respectively. Two exploratory control rounds comprise 680 executions on 40 independent test initial conditions. A matched reward revision on fresh test environments increases in-distribution 10 cm lifting success from 20.0% to 93.3%, while success within an 8 N per-finger budget reaches only 33.3%, compared with 70.0% for force feedback. Calibration margins reduce force violations at the cost of task completion. In a separate study of public GelSight recordings, a force regressor achieves 0.04234 N error, but frame-level calibration covers only 15.80% of complete trajectories; trajectory-level calibration raises this to 87.36% at nominal 90% coverage. Together, these findings distinguish improvements in sensing and task reward from improvements in force-constrained control. The evidence is limited to public sensing records and simulator execution, without a demonstrated transfer between them. Comments: 8 pages, 2 figures. Code and tabulated results included as ancillary material Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.09597 [cs.RO] (or arXiv:2609.09597v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.09597 arXiv-issued DOI via DataCite (pending registration) Submission history From: Qinzhen Ma [view email] [v1] Wed, 9 Sep 2026 01:50:54 UTC (1,108 KB) Full-text links: Access Paper: View a PDF of the paper titled Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints, by Qinzhen Ma (Rice University) and 1 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): ENVIRONMENT.json FILES_SHA256.json PACKAGING_CHANGES.md README.md SOURCE_MANIFEST.json THIRD_PARTY_NOTICES.md outputs/research/analyze_force_envelopes.py outputs/research/data_download_manifest.json outputs/research/download_public_data.py outputs/research/prepare_public_force_data.py 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