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Automated release from CI pipeline

Changes: feat(worldmodel): ADR-147 Phase 3+5 — RuViewOccDataset domain adapter + retraining pipeline

Phase 3 — scripts/ruview_occ_dataset.py:

  • RuViewOccDataset: WorldGraph JSON snapshots → OccWorld (F,H,W,D) tensors
  • Indoor class remapping: person→7, floor→9, wall→11, furniture→16, free→17
  • Zero ego-poses (fixed indoor sensor, no ego-motion)
  • record_snapshot() helper for training data accumulation
  • Validated: 5 windows, (16,200,200,16) tensor, person+floor voxels confirmed

Phase 5 — scripts/occworld_retrain.py:

  • record: stream WorldGraph snapshots from sensing server REST API
  • vqvae: fine-tune VQVAE tokenizer on RuView occupancy (200 epochs, AdamW)
  • transformer: fine-tune autoregressive transformer with frozen VQVAE

wifi-densepose-worldmodel v0.3.0 published to crates.io

Co-Authored-By: claude-flow ruv@ruv.net

Docker Image: ghcr.io/ruvnet/RuView:cd1c391afc815c5e9856f1a4173898357d6171ad

Source: README.md, updated 2026-05-29