Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints
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....
| Authors | Qinzhen Ma |
|---|---|
| Primary category | cs.RO |
| Published | 9 September 2026 |
| Last updated at source | 10 September 2026 |
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