We investigate a 652,157‑parameter action‑conditioned visuotactile world model that integrates behavior cloning, policy learning in imagination, independent implicit Q‑learning, and model‑assisted force feedback. A fixed protocol runs 34 policies on 120 fresh MuJoCo environments covering geometry and physical‑parameter shifts, and independently replays 324 action branches on 12 additional ID environments. Incorporating visuotactile dynamics reduces the force action‑effect MAE from 0.413 N for a persistence baseline to 0.338 N. Model‑assisted feedback raises the ID force‑budgeted success rate from 73.3% to 93.3%, with a paired difference of +20.0 [6.7,33.4] percentage points (95% CI), primarily during scripted lowering; the pooled difference is +3.9 [-4.5,+11.7] points. Imagined RL attains 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.
Review