feat: add vision transfer backbones and IMF variants

experiment_suites/2026-04-05-top-front-resnet-2cam/PLAN.md

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+# PLAN
+
+## Goal
+Train a 50k-step IMF baseline with the original ResNet vision backbone (no full-AttnRes vision replacement), using only `top` and `front` cameras as image conditioning.
+
+## Fixed comparison contract
+- Agent: `resnet_imf_attnres`
+- Vision backbone mode: `resnet`
+- `pred_horizon=16`
+- `num_action_steps=8`
+- `n_emb=384`, `n_layer=12`, `n_head=1`, `n_kv_head=1`
+- `inference_steps=1`
+- `batch_size=80`, `lr=2.5e-4`, cosine scheduler, warmup 2000
+- dataset: `/home/droid/project/diana_sim/sim_transfer`
+- cameras: `[top, front]` only
+- training budget: `max_steps=50000`
+- rollout validation: every 5 epochs, 5 episodes, headless
+
+## Resource plan
+- Host: local
+- GPU: RTX 5090 (GPU 0)
+
+## Execution path
+1. Run a short 2-step smoke test on GPU with the exact 2-camera config.
+2. If smoke passes, launch the 50k main run with durable log redirection.
+3. Record run name, pid, log path, and SwanLab URL into suite status.
+
+## Fallbacks
+- If batch 80 OOMs, fall back to batch 64 with scaled lr 2.0e-4.
+- If dataloader startup is unstable, reduce num_workers from 12 to 8.