loop-wam / LoopWAM

Closed-loop generalist policy improvement 
 from deployment rollouts.
LoopWAM turns real-world experience into better robot policies. By learning from successful, suboptimal, and failed rollouts alongside expert demonstrations, it closes the loop between deployment and improvement.
## ✨ Highlights
- **SOTA performance across simulation and real-world benchmarks.** LoopWAM achieves SOTA results on RoboChallenge Table30-V2, RoboTwin 2.0, and RoboCasa365.
- **Training on non-NVIDIA GPU.** All LoopWAM models reported across the three benchmarks are trained on **Alibaba Cloud PPU-810E**.
- **Nearly 2× the training speed of the FastWAM codebase.** Building on FastWAM's dataloader, we preprocess videos before training, stitching camera views and resizing them to the training resolution to make video decoding more efficient during data loading. We also optimize video decoding around the frames sampled for each training chunk. These optimizations reduce training time from **70+ hours to 40 hours**.
| Benchmark | Setting | LoopWAM |
| --- | --- | --- |
| RoboTwin 2.0 | 50 bimanual tasks, clean and randomized | **94.5%** average success |
| RoboCasa365 | 50 tasks, seen and unseen compositions | **49.5%** overall success |
| RoboChallenge Table30-V2 | 30 real tasks, 4 embodiments | **43.33%** success, **56.80** score |
No extra embodied-data pretraining is used on any of the three benchmarks, covering both simulation and real-world robots.
## 📑 Contents
- Highlights
- Results
 - RoboTwin 2.0
 - RoboCasa365
 - RoboChallenge Table30-V2
- Repository
- Environment
- Pretrained weights
- Dataset
 - RoboTwin 2.0
 - Real robots (Table30-V2)
- Training
- RoboTwin 2.0 evaluation
- RoboCasa365 evaluation
- RoboChallenge evaluation
## 🏆 Results
### 🤖 RoboTwin 2.0
50 bimanual tasks. Mixed expert–rollout training. Rollouts come from an expert-trained LoopWAM, X-VLA, and π0.5. The random seeds used for rollout collection differ from those used for evaluation.
| Method | Embodied pretraining | Clean | Randomized | Average |
| --- | --- | ---: | ---: | ---: |
| **LoopWAM** | No | **94.6** | **94.4** | **94.5** |
| LingBot-VA 2.0 | Yes | 93.8 | 93.4 | 93.6 |
| AHA-WAM | No | 93.4 | 92.2 | 92.8 |
| FastWAM | No | 91.9 | 91.8 | 91.8 |
| π0.5 | Yes | 82.7 | 76.8 | 79.8 |
### 🏠 RoboCasa365
All 16 Composite-Unseen tasks are held out of both expert data and rollout data.
| Method | Atomic seen | Composite seen | Composite unseen | Overall |
| --- | ---: | ---: | ---: | ---: |
| **LoopWAM** | **79.7** | **50.3** | 14.9 | **49.5** |
| ABot-M0.6 | 79.4 | 48.3 | 7.9 | 46.6 |
| PRTS | 66.3 | 30.3 | **18.8** | 39.6 |
### 🦾 RoboChallenge Table30-V2
30 real-world tasks on RoboChallenge. How to submit a run is in RoboChallenge evaluation.
| Method | ALOHA | DOS-W1 | ARX5 | UR5 | Score | SR |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| **LoopWAM** | **26.00** | **42.00** | 55.71 | **76.67** | **56.80** | **43.33** |
| VLA-DM0.5 | 18.00 | 42.00 | **58.57** | 70.00 | 54.42 | 40.67 |
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