Memory as Plans: World-Action Modeling with Memory-Grounded Planning
Most robot policies predict actions from the current observation or a short fixed window, yet long-horizon manipulation is non-Markovian: the evidence a decision needs may already be out of view. MaP-WAM treats memory as planning-time evidence instead of executor input. Completed segments are stored as structured multimodal records pairing a language instruction with sparse visual context (8 uniformly sampled frames per segment); a fine-tuned Qwen3.5-4B language planner proposes the next segment-level language plan, and a causal world model initialized from WAN-2.2-5B turns the long-term visual context into a matching visual plan. The two form a memory-grounded plan that conditions a World-Action-Progress executor, a Mixture-of-Transformers extension of a video DiT that jointly predicts action chunks, future visual latents, and execution progress, with progress as a first-class modality. Because the executor only sees a fixed-length plan prefix, its context stays constant as history grows, and block-causal attention makes both planning and execution KV-cacheable; plan-observation alignment calibrates recursively predicted progress against visual-plan frames, and a progress gate (threshold 0.95) triggers segment transitions that write resampled real observations back into memory. On RMBench MaP-WAM reaches 83.3% average success over nine memory-dependent tasks (best baseline LingBot-VA 77.1%), and 78.0% on two real Franka Research 3 tasks (88% Find Button, 68% Press Buttons), while a full-context executor runs out of memory at 1700 history frames and WAP holds an approximately constant 827 ms per action chunk. Limitations: segment structure is taken from benchmark annotations rather than discovered automatically, and plan-observation alignment uses a lightweight training-free pixel-difference metric.
