[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
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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)
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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
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outputs/simulation/audit_control_pairing.py
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outputs/simulation/run_policy_matrix.py
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outputs/world_model/LICENSE_TDMPC2.txt
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