dexgarmentlab-folding-lifting-dynamics-gns

• Task data: DexGarmentLab mixed-garment fold + lift-place demos (dexgarmentlab_folding_lifting_meshes.h5, Cloth-splatters/dexgarmentlab-folding-lifting-meshes)
• Formulation: direct acceleration regression (not diffusion), 15 message-passing steps, hidden size 128
• Velocity history (input_sequence_length): 5
• Training noise (velocity_noise_std): 0.1
• Best validation loss: 0.17032881488402685 (checkpoint in model/ is checkpoint-best)
• Training run: dexgarment_dyn_gns_k5_vn0p1_2026-08-04_20-30-52_466943 (full config in config.yml)

Why this configuration

• Longer history and training noise both help, and they compound. K=5 beats K=3 beats K=1 at every depth; within each K, more noise wins as the horizon grows. The optimal σ increases with horizon — σ=0.03 leads through frame 10, σ=0.1 from frame 15 on. For short-horizon use (MPC), prefer the σ=0.03 variant; for long rollouts, this one.
• One-shot error does not predict rollout quality. Single-step MSE spans only 1.4× across the whole grid (this model: 3.42e-5, mid-pack) while rollout MSE spans 3.5×. Selecting checkpoints on validation loss alone is unreliable here.

Caveats

• Training was stopped at 206k steps out of a planned 900k, so this is a deliberately truncated run rather than a converged one.
• The σ=0 cells in the grid stopped at 200k steps versus 206–208k for the noise cells (~3% fewer). The margins above are far larger than that gap, but the asymmetry exists.
• Validation loss is MSE in normalized acceleration units, and the normalizer's scale depends on velocity_noise_std — so the 0.170 figure above is not comparable to a checkpoint trained with a different σ.

Usage