license: apache-2.0
tags:
- robotics
- world-action-model
- libero
- loopwam
- chronoloop
ChronoLoop checkpoints
ChronoLoop adds a small recurrent memory to LoopWAM v0: 16 memory tokens ride through the
pre-blocks and the shared core loops with the observation, are read out after the last core
loop, squashed and mixed into a carried state, and are fed back at the next replanning query.
Non-memory tokens read the memory through a per-head gate tanh(alpha) that starts at 0, so
every run starts exactly at its parent.
Every run fine-tunes the same parent, anhdao69/LoopWAM_NT/libero-all-suites/v0-video4-action4
(LoopWAM v0, 4 video / 4 action core loops, 388/400 on the four LIBERO suites), on all four
LIBERO suites (1,712 demonstrations, 277,713 windows) for 10 epochs with global batch 128
(32 streams x 4 consecutive windows, truncated BPTT S = 4). The schedule, noise and data order
are shared by all runs, so they differ only in the memory flags.
| Folder | Run | Definition |
|---|---|---|
CL-0_no-memory_a4 |
CL-0 | No memory (control), K_a = 4 |
CL-A_mem16-learned-loopwrite_a4 |
CL-A | Memory written by the shared core loops (default ChronoLoop) |
CL-REG_mem16-reset-registers_a4 |
CL-REG | Same 16 tokens reset to s = 0 every query |
CL-W2_mem16-learned-external-updater_a4 |
CL-W2 | Read-only memory in the backbone; separate 1-block updater |
CL-0-at1_no-memory_a1 |
CL-0@1 | CL-0 with 1 action core loop |
CL-A-at1_mem16-learned-loopwrite_a1 |
CL-A@1 | CL-A with 1 action core loop |
CL-FRAME_history-frame-k3_a4 |
CL-FRAME | No memory; observation from query k-3 as extra gated clean frame |
Each folder has epoch_08/, epoch_09/ and epoch_10/ with policy.pt (weights only, format
chronoloop-v1) and config.json, plus the run's manifest.json, metrics.jsonl and timing.json.
A folder appears only after its training has written those epochs, and every upload is checked against
the local SHA-256.
Load with the chrono branch of LoopWAM_NT:
from fastwam.models.wan22.chronoloop import ChronoConfig, create_chronoloop
import torch
payload = torch.load('policy.pt', map_location='cpu', weights_only=False, mmap=True)
model, _ = create_chronoloop(ChronoConfig(**payload['chrono']), 'Wan2.1_VAE.pth', checkpoint_path='policy.pt', device='cuda')
# per replanning query: out = model.chrono_infer_action(image, proprio, context, mask, state=state); state = out['state']
Rollouts: scripts/evaluate_chronoloop_libero.py (700 policy steps, 30 settling steps, replan every 10 actions,
memory reset at episode start, fixed or fresh noise).