ChronoLoop on 5 hard RoboTwin 2.0 tasks (epoch 5)
Epoch-5 checkpoints of two ChronoLoop runs (the chrono branch of
anhdao69/LoopWAM_NT) trained on five RoboTwin 2.0 tasks:
| Folder | Run | Memory |
|---|---|---|
CL-0_no-memory_a4/ |
CL-0, the no-memory control | none (stateless) |
CL-A_mem16-learned-loopwrite_a4/ |
CL-A | 16 memory tokens, learned source, loop write, TBPTT S = 4 |
Both use LoopWAM-S v0 (3 pre + 6 core x 4 loops + 3 coda), K_a = 4, initialized from the full-suite LIBERO v0 4/4 parent (anhdao69/LoopWAM_NT) with new 14-D action and proprio interfaces.
These are mid-schedule checkpoints. The runs follow a 10-epoch cosine schedule (70,220 updates); epoch 5 ends at update 35,111, where the learning rate is still 53% of its peak.
Data
Tasks: hanging_mug, blocks_ranking_size, scan_object, place_can_basket, handover_mic — the five tasks with the lowest
trimmed-mean success in LiLa-WAM (arXiv 2608.03701) Table 8. 550 demonstrations per task (50 clean + 500
randomized) from the FastWAM RoboTwin release: 2,750 episodes, 792,168 training windows. FastWAM RoboTwin input
protocol: 9 frames (offsets 0..32 step 4) and 32 actions per window, three cameras packed into 384x320, 14-D joint
actions and state, global z-score with the published FastWAM statistics (data/dataset_stats.json).
Files
<run>/epoch_05/policy.pt— weights only (chronoloop-v1), FP32.<run>/epoch_05/config.json— memory flags, parent and schedule hashes, the epoch's last update.<run>/manifest.json,metrics.jsonl,timing.json— the full training record (per-update losses, LR, gates).data/dataset_stats.json(normalization),data/data_manifest.json(episodes, windows, protocol).
Load with fastwam.models.wan22.chronoloop_robot.create_chronoloop_robot(cfg, vae_path, action_dim=14, proprio_dim=14, checkpoint_path=".../policy.pt"), where cfg is the ChronoConfig stored in the checkpoint.
Training
LoopWAM-S v0, K_a = 4; AdamW (0.9, 0.95), weight decay 0.01; LR 3e-5 backbone, 3e-4 memory and robot interfaces; 3% warmup and cosine to 1% over 10 epochs (70,220 updates of 32 streams x 4 windows); TBPTT over 4 windows with stride 24. Two H100s per run.
| Epoch | CL-0 loss (video / action) | CL-A loss (video / action) |
|---|---|---|
| 1 | 0.1014 / 0.1139 | 0.1014 / 0.1139 |
| 2 | 0.0756 / 0.0170 | 0.0756 / 0.0170 |
| 3 | 0.0691 / 0.0113 | 0.0690 / 0.0110 |
| 4 | 0.0651 / 0.0085 | 0.0648 / 0.0072 |
| 5 | 0.0623 / 0.0067 | 0.0618 / 0.0051 |
Per-epoch means of the training loss. CL-A's memory read gates start at zero, so the two runs coincide for two epochs; CL-A's action loss is 24% lower in epoch 5.
RoboTwin 2.0 evaluation
RoboTwin 2.0 harness (script/eval_policy.py, aloha-agilex), unseen instructions, 50 episodes per task in each
setting (scene seeds from 100000, kept only if the expert planner solves them; step limits from RoboTwin's task
configs). The policy uses the training input protocol, executes the first 24 of 32 predicted joint targets and replans;
10 Euler steps, no CFG. CL-A's memory resets each episode.
Mean success over the 5 tasks (mean of per-task rates):
| Run | Clean | Randomized |
|---|---|---|
| CL-0 (no memory) | 55.2% | 50.0% |
| CL-A (mem16) | 57.6% | 53.1% |
| Task | CL-0 Clean | CL-A Clean | CL-0 Randomized | CL-A Randomized |
|---|---|---|---|---|
| hanging_mug | 12/50 | 15/50 | 15/50 | 14/49 |
| blocks_ranking_size | 24/50 | 23/50 | 18/50 | 18/50 |
| scan_object | 30/50 | 29/50 | 22/50 | 25/50 |
| place_can_basket | 22/50 | 27/48 | 21/50 | 25/49 |
| handover_mic | 50/50 | 49/50 | 49/50 | 50/50 |
(CL-A's evaluation ran each job as two halves; in three jobs one or two seeds were run by both halves and count once.)
On the 220 scenes per setting that both models ran, CL-A leads by 1.3 points on Clean and 5.4 on Randomized; neither
difference is statistically significant (exact McNemar p = 0.80 and 0.14). Per-seed outcomes:
eval/robotwin5hard_epoch05_results.json. These models are trained on 5 tasks only, so their numbers are not
comparable to multi-task results on all 50 RoboTwin tasks.
Report: plans/performance/ChronoLoop_Robotwin5hard_Epoch5_20261017.md on the chrono branch (training, evaluation protocol, harness changes, environment and commands).