Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints

• URL: http://arxiv.org/abs/2609.09597v2
• Date: Thu, 10 Sep 2026 04:45:50 GMT
• Title: Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints
• Abstract summary: Visuotactile dynamics reduce force action-effect MAE from 0.413 N for persistence to 0.338 N.<n>Model-assisted feedback raises ID force-budgeted success from 73.3% to 93.3%.<n> Imagined RL achieves 11.9% pooled joint success versus 25.0% for reactive IQL.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Accurate tactile forecasts need not improve force-constrained control. We study a 652,157-parameter action-conditioned visuotactile world model with matched behavior cloning, policy learning in imagination, independent reactive implicit Q-learning, and model-assisted force feedback. A fixed protocol executes 34 policies on 120 fresh MuJoCo environments spanning geometry and physical-parameter shifts, plus 324 independently replayed action branches on 12 additional ID environments. Visuotactile dynamics reduce force action-effect MAE from 0.413 N for persistence to 0.338 N. Model-assisted feedback raises ID force-budgeted success from 73.3% to 93.3%, with paired difference +20.0 [+6.7,+33.4] percentage points (95% CI), with the difference occurring during scripted lowering. Its pooled difference is +3.9 [-4.5,+11.7] points. Imagined RL achieves 11.9% pooled joint success versus 25.0% for reactive IQL. An empirical tactile-residual stress test adds 330 executions. The evidence concerns rigid-box lifting after a common approach, without physical-robot transfer or a closed-loop safety guarantee.

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