Datasets:

loop-wam
/

loopwam

---
pretty_name: LoopWAM checkpoints
---
# LoopWAM

**Closed-loop generalist policy improvement from deployment rollouts.**

[Project page](https://loopwam.github.io/) · [Code](https://github.com/loop-wam/LoopWAM)

LoopWAM turns real-world experience into better robot policies. Expert demonstrations sit alongside successful, suboptimal, and failed rollouts. Every rollout teaches the world model. Action imitation is weighted by quality, and failed actions are not copied.

![LoopWAM self-improvement loop: deploy, collect mixed-quality rollouts, and improve through quality-aware learning.](https://loopwam.github.io/assets/branding/teaser.webp)

<video src="https://loopwam.github.io/assets/videos/overview.mp4" poster="https://loopwam.github.io/assets/posters/overview.jpg" controls playsinline width="100%"></video>

## Method

![Mixed-quality expert and rollout data train video and action branches through joint attention, source-dependent noise, quality-weighted supervision, and quality steering.](https://loopwam.github.io/assets/figures/method.webp)

Video and action tokens interact through bidirectional attention while they are denoised together.

- **Joint video–action modeling.** The two streams attend to each other at every layer.
- **Source-dependent noise.** Lower-noise rollout actions guide video prediction toward the physical consequences that were observed.
- **Quality-aware learning.** Quality conditions and weights guide action learning. Every rollout teaches video prediction, without imitating failed actions. At deployment, an expert-quality target steers the policy.

## Leaderboards

Best overall performance among the methods compared in the paper.

| Benchmark | LoopWAM |
| --- | --- |
| RoboChallenge Table30-V2 | **43.33%** success · **56.80** score |
| RoboTwin 2.0 | **94.5%** average |
| RoboCasa365 | **49.5%** overall |

### RoboChallenge Table30-V2

Real-world, 30 tasks, 4 embodiments. One multi-task model per embodiment. Overall score uses task weights 10 / 10 / 7 / 3.

| 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 |
| my16 | 13.00 | 27.00 | 54.29 | 46.67 | 41.27 | 30.67 |
| LoopWAM (expert-trained) | 8.00 | 38.00 | 24.29 | 26.67 | 36.10 | 23.67 |
| JV0 | 5.00 | 16.00 | 20.00 | 43.33 | 27.58 | 16.00 |
| mc | 7.00 | 0.00 | 47.14 | 20.00 | 23.63 | 15.33 |
| MagicBot | 0.00 | 5.00 | 32.86 | 53.33 | 20.82 | 14.67 |
| holobrain | 3.00 | 6.00 | 30.00 | 43.33 | 27.93 | 14.33 |
| Baseline Model | 6.00 | 6.00 | 32.86 | 26.67 | 31.48 | 14.33 |
| test_01 | 1.00 | 3.00 | 28.57 | 43.33 | 22.73 | 12.33 |
| W0 | 2.00 | 0.00 | 30.00 | 43.33 | 21.85 | 12.00 |
| debuggers_vla | 0.00 | 2.00 | 34.29 | 23.33 | 18.72 | 11.00 |

Columns ALOHA, DOS-W1, ARX5, and UR5 are success rates (%).

### RoboTwin 2.0

50 bimanual tasks. No additional embodied pretraining. Clean 94.6%, randomized 94.4%.

| 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 |
| LingBot-VA | Yes | 92.9 | 91.5 | 92.2 |
| Fast-WAM | No | 91.9 | 91.8 | 91.8 |
| Motus | Yes | 88.7 | 87.0 | 87.8 |
| ABot-M0 | Yes | 81.2 | 80.4 | 80.8 |
| π0.5 | Yes | 82.7 | 76.8 | 79.8 |
| Motus (from Wan2.2) | No | 77.6 | 77.0 | 77.3 |
| X-VLA | Yes | 72.9 | 72.8 | 72.9 |
| π0 | Yes | 65.9 | 58.4 | 62.2 |

### RoboCasa365

50 evaluation tasks. Overall success uses weights 18 / 16 / 16. All 16 Composite-Unseen tasks are held out of training.

| Method | Embodied pretraining | Atomic seen | Composite seen | Composite unseen | Overall |
| --- | --- | ---: | ---: | ---: | ---: |
| **LoopWAM** | No | **79.7** | **50.3** | 14.9 | **49.5** |
| ABot-M0.6 | Yes | 79.4 | 48.3 | 7.9 | 46.6 |
| ABot-M0.5 | Yes | 75.6 | 37.7 | 3.3 | 40.3 |
| PRTS | Yes | 66.3 | 30.3 | **18.8** | 39.6 |
| RLDX-1 | Yes | 67.6 | 27.9 | 8.5 | 36.0 |
| GR00T N1.5 | Yes | 50.7 | 14.8 | 2.7 | 23.9 |
| GR00T N1.6 | Yes | 51.1 | 9.4 | 1.7 | 21.9 |
| GigaWorld-Policy 0.1 | Yes | 44.4 | 11.8 | 2.9 | 20.7 |
| π0.5 | Yes | 39.6 | 7.1 | 1.2 | 16.9 |
| π0 | Yes | 34.6 | 6.1 | 1.1 | 14.8 |
| Diffusion Policy | No | 15.7 | 0.2 | 1.3 | 6.1 |

## Multi-task self-improvement

30 tasks, four embodiments, three rounds of real-world deployment. The clip below is 30 full rollouts, sped up to 30 seconds.

<video src="https://loopwam.github.io/assets/videos/rollout-30-tasks.mp4" poster="https://loopwam.github.io/assets/posters/rollout-30-tasks.jpg" controls playsinline width="100%"></video>

| Stage | Success | Score |
| --- | ---: | ---: |
| Expert-trained | 23.67% | 36.10 |
| Round 1 | 28.00% | 42.63 |
| Round 2 | 37.67% | 51.65 |
| Round 3 | 43.33% | 56.80 |

### Tasks

Click a still to open the rollout video.

**ALOHA**

[![Lint Roller](https://loopwam.github.io/assets/posters/tasks/lint_roller_remove_dirt.jpg)](https://loopwam.github.io/assets/videos/tasks/lint_roller_remove_dirt.mp4)
[![Spread Jam](https://loopwam.github.io/assets/posters/tasks/paint_jam.jpg)](https://loopwam.github.io/assets/videos/tasks/paint_jam.mp4)
[![Put Books Back](https://loopwam.github.io/assets/posters/tasks/put_the_books_back.jpg)](https://loopwam.github.io/assets/videos/tasks/put_the_books_back.mp4)
[![Scoop with a Spoon](https://loopwam.github.io/assets/posters/tasks/scoop_with_a_small_spoon.jpg)](https://loopwam.github.io/assets/videos/tasks/scoop_with_a_small_spoon.mp4)
[![Stamp Positioning](https://loopwam.github.io/assets/posters/tasks/stamp_positioning.jpg)](https://loopwam.github.io/assets/videos/tasks/stamp_positioning.mp4)
[![Wipe Blackboard](https://loopwam.github.io/assets/posters/tasks/wipe_the_blackboard.jpg)](https://loopwam.github.io/assets/videos/tasks/wipe_the_blackboard.mp4)
[![Wrap with Cloth](https://loopwam.github.io/assets/posters/tasks/wrap_with_a_soft_cloth.jpg)](https://loopwam.github.io/assets/videos/tasks/wrap_with_a_soft_cloth.mp4)

**ARX5**

[![Arrange Flowers](https://loopwam.github.io/assets/posters/tasks/arrange_flowers.jpg)](https://loopwam.github.io/assets/videos/tasks/arrange_flowers.mp4)
[![Hang the Cup](https://loopwam.github.io/assets/posters/tasks/hang_the_cup.jpg)](https://loopwam.github.io/assets/videos/tasks/hang_the_cup.mp4)
[![Pick Green Blocks](https://loopwam.github.io/assets/posters/tasks/pick_out_the_green_blocks.jpg)](https://loopwam.github.io/assets/videos/tasks/pick_out_the_green_blocks.mp4)
[![Press the Button](https://loopwam.github.io/assets/posters/tasks/press_the_button.jpg)](https://loopwam.github.io/assets/videos/tasks/press_the_button.mp4)
[![Light Switch](https://loopwam.github.io/assets/posters/tasks/turn_on_the_light_switch.jpg)](https://loopwam.github.io/assets/videos/tasks/turn_on_the_light_switch.mp4)
[![Water Flowers](https://loopwam.github.io/assets/posters/tasks/water_the_flowers.jpg)](https://loopwam.github.io/assets/videos/tasks/water_the_flowers.mp4)
[![Wipe the Table](https://loopwam.github.io/assets/posters/tasks/wipe_the_table.jpg)](https://loopwam.github.io/assets/videos/tasks/wipe_the_table.mp4)

**UR5**

[![Arrange Fruits](https://loopwam.github.io/assets/posters/tasks/arrange_fruits.jpg)](https://loopwam.github.io/assets/videos/tasks/arrange_fruits.mp4)
[![Item Classification](https://loopwam.github.io/assets/posters/tasks/item_classification.jpg)](https://loopwam.github.io/assets/videos/tasks/item_classification.mp4)
[![Shred Paper](https://loopwam.github.io/assets/posters/tasks/shred_paper.jpg)](https://loopwam.github.io/assets/videos/tasks/shred_paper.mp4)

**DOS-W1**

[![Hold the Tray](https://loopwam.github.io/assets/posters/tasks/hold_the_tray_with_both_hands.jpg)](https://loopwam.github.io/assets/videos/tasks/hold_the_tray_with_both_hands.mp4)
[![Desk Drawer](https://loopwam.github.io/assets/posters/tasks/place_objects_into_desk_drawer.jpg)](https://loopwam.github.io/assets/videos/tasks/place_objects_into_desk_drawer.mp4)
[![Pen Container](https://loopwam.github.io/assets/posters/tasks/put_in_pen_container.jpg)](https://loopwam.github.io/assets/videos/tasks/put_in_pen_container.mp4)
[![Put Shoes Back](https://loopwam.github.io/assets/posters/tasks/put_the_shoes_back.jpg)](https://loopwam.github.io/assets/videos/tasks/put_the_shoes_back.mp4)
[![Stack Bowls](https://loopwam.github.io/assets/posters/tasks/stack_bowls.jpg)](https://loopwam.github.io/assets/videos/tasks/stack_bowls.mp4)
[![Sweep the Trash](https://loopwam.github.io/assets/posters/tasks/sweep_the_trash.jpg)](https://loopwam.github.io/assets/videos/tasks/sweep_the_trash.mp4)
[![Makeup Table](https://loopwam.github.io/assets/posters/tasks/tidy_up_the_makeup_table.jpg)](https://loopwam.github.io/assets/videos/tasks/tidy_up_the_makeup_table.mp4)
[![Tie a Knot](https://loopwam.github.io/assets/posters/tasks/tie_a_knot.jpg)](https://loopwam.github.io/assets/videos/tasks/tie_a_knot.mp4)

## Shirt folding

After expert post-training, two deployment rounds improve DOS-W1 shirt folding from 13.20% to 55.56%.

| Stage | Training data | Success | Selected trials |
| --- | --- | ---: | --- |
| Stage 1 | Expert demonstrations | 13.20% | 1 success, 8 failures |
| Stage 2 | Expert + round 1 rollouts | 32.54% | 3 successes, 6 failures |
| Stage 3 | Expert + rounds 1 and 2 | 55.56% | 5 successes, 4 failures |

Stage 1, the success:

[![Stage 1 success](https://loopwam.github.io/assets/posters/folding/s16.jpg)](https://loopwam.github.io/assets/videos/folding/s16.mp4)

Stage 2 successes:

[![Stage 2](https://loopwam.github.io/assets/posters/folding/s11.jpg)](https://loopwam.github.io/assets/videos/folding/s11.mp4)
[![Stage 2](https://loopwam.github.io/assets/posters/folding/s10.jpg)](https://loopwam.github.io/assets/videos/folding/s10.mp4)
[![Stage 2](https://loopwam.github.io/assets/posters/folding/s9.jpg)](https://loopwam.github.io/assets/videos/folding/s9.mp4)

Stage 3 successes:

[![Stage 3](https://loopwam.github.io/assets/posters/folding/s5.jpg)](https://loopwam.github.io/assets/videos/folding/s5.mp4)
[![Stage 3](https://loopwam.github.io/assets/posters/folding/s4.jpg)](https://loopwam.github.io/assets/videos/folding/s4.mp4)
[![Stage 3](https://loopwam.github.io/assets/posters/folding/s3.jpg)](https://loopwam.github.io/assets/videos/folding/s3.mp4)
[![Stage 3](https://loopwam.github.io/assets/posters/folding/s2.jpg)](https://loopwam.github.io/assets/videos/folding/s2.mp4)
[![Stage 3](https://loopwam.github.io/assets/posters/folding/s1.jpg)](https://loopwam.github.io/assets/videos/folding/s1.mp4)

## Action and video generation

Each pair is the observed video on the left and LoopWAM’s prediction on the right.

**Arrange Fruits**

[![Arrange Fruits, observed](https://loopwam.github.io/assets/posters/generation/arrange_fruits_episode_000000.jpg)](https://loopwam.github.io/assets/videos/generation/arrange_fruits_episode_000000.mp4)
[![Arrange Fruits, predicted](https://loopwam.github.io/assets/posters/generation/arrange_fruits_episode_000002.jpg)](https://loopwam.github.io/assets/videos/generation/arrange_fruits_episode_000002.mp4)

**Pen Container**

[![Pen Container, observed](https://loopwam.github.io/assets/posters/generation/put_in_pen_container_episode_000001.jpg)](https://loopwam.github.io/assets/videos/generation/put_in_pen_container_episode_000001.mp4)
[![Pen Container, predicted](https://loopwam.github.io/assets/posters/generation/put_in_pen_container_episode_000003.jpg)](https://loopwam.github.io/assets/videos/generation/put_in_pen_container_episode_000003.mp4)

**Stack Bowls**

[![Stack Bowls, observed](https://loopwam.github.io/assets/posters/generation/stack_bowls_episode_000005.jpg)](https://loopwam.github.io/assets/videos/generation/stack_bowls_episode_000005.mp4)
[![Stack Bowls, predicted](https://loopwam.github.io/assets/posters/generation/stack_bowls_episode_000007.jpg)](https://loopwam.github.io/assets/videos/generation/stack_bowls_episode_000007.mp4)

**Lint Roller**

[![Lint Roller, observed](https://loopwam.github.io/assets/posters/generation/lint_roller_remove_dirt_episode_000004.jpg)](https://loopwam.github.io/assets/videos/generation/lint_roller_remove_dirt_episode_000004.mp4)
[![Lint Roller, predicted](https://loopwam.github.io/assets/posters/generation/lint_roller_remove_dirt_episode_000006.jpg)](https://loopwam.github.io/assets/videos/generation/lint_roller_remove_dirt_episode_000006.mp4)

## Checkpoints

Each directory contains one weight file and its `dataset_stats.json`.

| Benchmark | Directory | Checkpoint |
| --- | --- | --- |
| Table30-V2, UR5 | `table30v2_ur5/` | `step_029925.pt` |
| Table30-V2, ALOHA | `table30v2_aloha/` | `step_061320.pt` |
| Table30-V2, ARX5 | `table30v2_arx5/` | `step_057355.pt` |
| Table30-V2, DOS-W1 | `table30v2_w1/` | `step_072970.pt` |
| RoboTwin 2.0 | `robotwin/` | `step_037645.pt` |

	---
	pretty_name: LoopWAM checkpoints
	---

	# LoopWAM

	Closed-loop generalist policy improvement from deployment rollouts.

	[Project page](https://loopwam.github.io/) · [Code](https://github.com/loop-wam/LoopWAM)

	LoopWAM turns real-world experience into better robot policies. Expert demonstrations sit alongside successful, suboptimal, and failed rollouts. Every rollout teaches the world model. Action imitation is weighted by quality, and failed actions are not copied.

	![LoopWAM self-improvement loop: deploy, collect mixed-quality rollouts, and improve through quality-aware learning.](https://loopwam.github.io/assets/branding/teaser.webp)

	<video src="https://loopwam.github.io/assets/videos/overview.mp4" poster="https://loopwam.github.io/assets/posters/overview.jpg" controls playsinline width="100%"></video>

	## Method

	![Mixed-quality expert and rollout data train video and action branches through joint attention, source-dependent noise, quality-weighted supervision, and quality steering.](https://loopwam.github.io/assets/figures/method.webp)

	Video and action tokens interact through bidirectional attention while they are denoised together.

	- Joint video–action modeling. The two streams attend to each other at every layer.
	- Source-dependent noise. Lower-noise rollout actions guide video prediction toward the physical consequences that were observed.
	- Quality-aware learning. Quality conditions and weights guide action learning. Every rollout teaches video prediction, without imitating failed actions. At deployment, an expert-quality target steers the policy.

	## Leaderboards

	Best overall performance among the methods compared in the paper.

	\| Benchmark \| LoopWAM \|
	\| --- \| --- \|
	\| RoboChallenge Table30-V2 \| 43.33% success · 56.80 score \|
	\| RoboTwin 2.0 \| 94.5% average \|
	\| RoboCasa365 \| 49.5% overall \|

	### RoboChallenge Table30-V2

	Real-world, 30 tasks, 4 embodiments. One multi-task model per embodiment. Overall score uses task weights 10 / 10 / 7 / 3.

	\| 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 \|
	\| my16 \| 13.00 \| 27.00 \| 54.29 \| 46.67 \| 41.27 \| 30.67 \|
	\| LoopWAM (expert-trained) \| 8.00 \| 38.00 \| 24.29 \| 26.67 \| 36.10 \| 23.67 \|
	\| JV0 \| 5.00 \| 16.00 \| 20.00 \| 43.33 \| 27.58 \| 16.00 \|
	\| mc \| 7.00 \| 0.00 \| 47.14 \| 20.00 \| 23.63 \| 15.33 \|
	\| MagicBot \| 0.00 \| 5.00 \| 32.86 \| 53.33 \| 20.82 \| 14.67 \|
	\| holobrain \| 3.00 \| 6.00 \| 30.00 \| 43.33 \| 27.93 \| 14.33 \|
	\| Baseline Model \| 6.00 \| 6.00 \| 32.86 \| 26.67 \| 31.48 \| 14.33 \|
	\| test_01 \| 1.00 \| 3.00 \| 28.57 \| 43.33 \| 22.73 \| 12.33 \|
	\| W0 \| 2.00 \| 0.00 \| 30.00 \| 43.33 \| 21.85 \| 12.00 \|
	\| debuggers_vla \| 0.00 \| 2.00 \| 34.29 \| 23.33 \| 18.72 \| 11.00 \|

	Columns ALOHA, DOS-W1, ARX5, and UR5 are success rates (%).

	### RoboTwin 2.0

	50 bimanual tasks. No additional embodied pretraining. Clean 94.6%, randomized 94.4%.

	\| 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 \|
	\| LingBot-VA \| Yes \| 92.9 \| 91.5 \| 92.2 \|
	\| Fast-WAM \| No \| 91.9 \| 91.8 \| 91.8 \|
	\| Motus \| Yes \| 88.7 \| 87.0 \| 87.8 \|
	\| ABot-M0 \| Yes \| 81.2 \| 80.4 \| 80.8 \|
	\| π0.5 \| Yes \| 82.7 \| 76.8 \| 79.8 \|
	\| Motus (from Wan2.2) \| No \| 77.6 \| 77.0 \| 77.3 \|
	\| X-VLA \| Yes \| 72.9 \| 72.8 \| 72.9 \|
	\| π0 \| Yes \| 65.9 \| 58.4 \| 62.2 \|

	### RoboCasa365

	50 evaluation tasks. Overall success uses weights 18 / 16 / 16. All 16 Composite-Unseen tasks are held out of training.

	\| Method \| Embodied pretraining \| Atomic seen \| Composite seen \| Composite unseen \| Overall \|
	\| --- \| --- \| ---: \| ---: \| ---: \| ---: \|
	\| LoopWAM \| No \| 79.7 \| 50.3 \| 14.9 \| 49.5 \|
	\| ABot-M0.6 \| Yes \| 79.4 \| 48.3 \| 7.9 \| 46.6 \|
	\| ABot-M0.5 \| Yes \| 75.6 \| 37.7 \| 3.3 \| 40.3 \|
	\| PRTS \| Yes \| 66.3 \| 30.3 \| 18.8 \| 39.6 \|
	\| RLDX-1 \| Yes \| 67.6 \| 27.9 \| 8.5 \| 36.0 \|
	\| GR00T N1.5 \| Yes \| 50.7 \| 14.8 \| 2.7 \| 23.9 \|
	\| GR00T N1.6 \| Yes \| 51.1 \| 9.4 \| 1.7 \| 21.9 \|
	\| GigaWorld-Policy 0.1 \| Yes \| 44.4 \| 11.8 \| 2.9 \| 20.7 \|
	\| π0.5 \| Yes \| 39.6 \| 7.1 \| 1.2 \| 16.9 \|
	\| π0 \| Yes \| 34.6 \| 6.1 \| 1.1 \| 14.8 \|
	\| Diffusion Policy \| No \| 15.7 \| 0.2 \| 1.3 \| 6.1 \|

	## Multi-task self-improvement

	30 tasks, four embodiments, three rounds of real-world deployment. The clip below is 30 full rollouts, sped up to 30 seconds.

	<video src="https://loopwam.github.io/assets/videos/rollout-30-tasks.mp4" poster="https://loopwam.github.io/assets/posters/rollout-30-tasks.jpg" controls playsinline width="100%"></video>

	\| Stage \| Success \| Score \|
	\| --- \| ---: \| ---: \|
	\| Expert-trained \| 23.67% \| 36.10 \|
	\| Round 1 \| 28.00% \| 42.63 \|
	\| Round 2 \| 37.67% \| 51.65 \|
	\| Round 3 \| 43.33% \| 56.80 \|

	### Tasks

	Click a still to open the rollout video.

	ALOHA

	[![Lint Roller](https://loopwam.github.io/assets/posters/tasks/lint_roller_remove_dirt.jpg)](https://loopwam.github.io/assets/videos/tasks/lint_roller_remove_dirt.mp4)
	[![Spread Jam](https://loopwam.github.io/assets/posters/tasks/paint_jam.jpg)](https://loopwam.github.io/assets/videos/tasks/paint_jam.mp4)
	[![Put Books Back](https://loopwam.github.io/assets/posters/tasks/put_the_books_back.jpg)](https://loopwam.github.io/assets/videos/tasks/put_the_books_back.mp4)
	[![Scoop with a Spoon](https://loopwam.github.io/assets/posters/tasks/scoop_with_a_small_spoon.jpg)](https://loopwam.github.io/assets/videos/tasks/scoop_with_a_small_spoon.mp4)
	[![Stamp Positioning](https://loopwam.github.io/assets/posters/tasks/stamp_positioning.jpg)](https://loopwam.github.io/assets/videos/tasks/stamp_positioning.mp4)
	[![Wipe Blackboard](https://loopwam.github.io/assets/posters/tasks/wipe_the_blackboard.jpg)](https://loopwam.github.io/assets/videos/tasks/wipe_the_blackboard.mp4)
	[![Wrap with Cloth](https://loopwam.github.io/assets/posters/tasks/wrap_with_a_soft_cloth.jpg)](https://loopwam.github.io/assets/videos/tasks/wrap_with_a_soft_cloth.mp4)

	ARX5

	[![Arrange Flowers](https://loopwam.github.io/assets/posters/tasks/arrange_flowers.jpg)](https://loopwam.github.io/assets/videos/tasks/arrange_flowers.mp4)
	[![Hang the Cup](https://loopwam.github.io/assets/posters/tasks/hang_the_cup.jpg)](https://loopwam.github.io/assets/videos/tasks/hang_the_cup.mp4)
	[![Pick Green Blocks](https://loopwam.github.io/assets/posters/tasks/pick_out_the_green_blocks.jpg)](https://loopwam.github.io/assets/videos/tasks/pick_out_the_green_blocks.mp4)
	[![Press the Button](https://loopwam.github.io/assets/posters/tasks/press_the_button.jpg)](https://loopwam.github.io/assets/videos/tasks/press_the_button.mp4)
	[![Light Switch](https://loopwam.github.io/assets/posters/tasks/turn_on_the_light_switch.jpg)](https://loopwam.github.io/assets/videos/tasks/turn_on_the_light_switch.mp4)
	[![Water Flowers](https://loopwam.github.io/assets/posters/tasks/water_the_flowers.jpg)](https://loopwam.github.io/assets/videos/tasks/water_the_flowers.mp4)
	[![Wipe the Table](https://loopwam.github.io/assets/posters/tasks/wipe_the_table.jpg)](https://loopwam.github.io/assets/videos/tasks/wipe_the_table.mp4)

	UR5

	[![Arrange Fruits](https://loopwam.github.io/assets/posters/tasks/arrange_fruits.jpg)](https://loopwam.github.io/assets/videos/tasks/arrange_fruits.mp4)
	[![Item Classification](https://loopwam.github.io/assets/posters/tasks/item_classification.jpg)](https://loopwam.github.io/assets/videos/tasks/item_classification.mp4)
	[![Shred Paper](https://loopwam.github.io/assets/posters/tasks/shred_paper.jpg)](https://loopwam.github.io/assets/videos/tasks/shred_paper.mp4)

	DOS-W1

	[![Hold the Tray](https://loopwam.github.io/assets/posters/tasks/hold_the_tray_with_both_hands.jpg)](https://loopwam.github.io/assets/videos/tasks/hold_the_tray_with_both_hands.mp4)
	[![Desk Drawer](https://loopwam.github.io/assets/posters/tasks/place_objects_into_desk_drawer.jpg)](https://loopwam.github.io/assets/videos/tasks/place_objects_into_desk_drawer.mp4)
	[![Pen Container](https://loopwam.github.io/assets/posters/tasks/put_in_pen_container.jpg)](https://loopwam.github.io/assets/videos/tasks/put_in_pen_container.mp4)
	[![Put Shoes Back](https://loopwam.github.io/assets/posters/tasks/put_the_shoes_back.jpg)](https://loopwam.github.io/assets/videos/tasks/put_the_shoes_back.mp4)
	[![Stack Bowls](https://loopwam.github.io/assets/posters/tasks/stack_bowls.jpg)](https://loopwam.github.io/assets/videos/tasks/stack_bowls.mp4)
	[![Sweep the Trash](https://loopwam.github.io/assets/posters/tasks/sweep_the_trash.jpg)](https://loopwam.github.io/assets/videos/tasks/sweep_the_trash.mp4)
	[![Makeup Table](https://loopwam.github.io/assets/posters/tasks/tidy_up_the_makeup_table.jpg)](https://loopwam.github.io/assets/videos/tasks/tidy_up_the_makeup_table.mp4)
	[![Tie a Knot](https://loopwam.github.io/assets/posters/tasks/tie_a_knot.jpg)](https://loopwam.github.io/assets/videos/tasks/tie_a_knot.mp4)

	## Shirt folding

	After expert post-training, two deployment rounds improve DOS-W1 shirt folding from 13.20% to 55.56%.

	\| Stage \| Training data \| Success \| Selected trials \|
	\| --- \| --- \| ---: \| --- \|
	\| Stage 1 \| Expert demonstrations \| 13.20% \| 1 success, 8 failures \|
	\| Stage 2 \| Expert + round 1 rollouts \| 32.54% \| 3 successes, 6 failures \|
	\| Stage 3 \| Expert + rounds 1 and 2 \| 55.56% \| 5 successes, 4 failures \|

	Stage 1, the success:

	[![Stage 1 success](https://loopwam.github.io/assets/posters/folding/s16.jpg)](https://loopwam.github.io/assets/videos/folding/s16.mp4)

	Stage 2 successes:

	[![Stage 2](https://loopwam.github.io/assets/posters/folding/s11.jpg)](https://loopwam.github.io/assets/videos/folding/s11.mp4)
	[![Stage 2](https://loopwam.github.io/assets/posters/folding/s10.jpg)](https://loopwam.github.io/assets/videos/folding/s10.mp4)
	[![Stage 2](https://loopwam.github.io/assets/posters/folding/s9.jpg)](https://loopwam.github.io/assets/videos/folding/s9.mp4)

	Stage 3 successes:

	[![Stage 3](https://loopwam.github.io/assets/posters/folding/s5.jpg)](https://loopwam.github.io/assets/videos/folding/s5.mp4)
	[![Stage 3](https://loopwam.github.io/assets/posters/folding/s4.jpg)](https://loopwam.github.io/assets/videos/folding/s4.mp4)
	[![Stage 3](https://loopwam.github.io/assets/posters/folding/s3.jpg)](https://loopwam.github.io/assets/videos/folding/s3.mp4)
	[![Stage 3](https://loopwam.github.io/assets/posters/folding/s2.jpg)](https://loopwam.github.io/assets/videos/folding/s2.mp4)
	[![Stage 3](https://loopwam.github.io/assets/posters/folding/s1.jpg)](https://loopwam.github.io/assets/videos/folding/s1.mp4)

	## Action and video generation

	Each pair is the observed video on the left and LoopWAM’s prediction on the right.

	Arrange Fruits

	[![Arrange Fruits, observed](https://loopwam.github.io/assets/posters/generation/arrange_fruits_episode_000000.jpg)](https://loopwam.github.io/assets/videos/generation/arrange_fruits_episode_000000.mp4)
	[![Arrange Fruits, predicted](https://loopwam.github.io/assets/posters/generation/arrange_fruits_episode_000002.jpg)](https://loopwam.github.io/assets/videos/generation/arrange_fruits_episode_000002.mp4)

	Pen Container

	[![Pen Container, observed](https://loopwam.github.io/assets/posters/generation/put_in_pen_container_episode_000001.jpg)](https://loopwam.github.io/assets/videos/generation/put_in_pen_container_episode_000001.mp4)
	[![Pen Container, predicted](https://loopwam.github.io/assets/posters/generation/put_in_pen_container_episode_000003.jpg)](https://loopwam.github.io/assets/videos/generation/put_in_pen_container_episode_000003.mp4)

	Stack Bowls

	[![Stack Bowls, observed](https://loopwam.github.io/assets/posters/generation/stack_bowls_episode_000005.jpg)](https://loopwam.github.io/assets/videos/generation/stack_bowls_episode_000005.mp4)
	[![Stack Bowls, predicted](https://loopwam.github.io/assets/posters/generation/stack_bowls_episode_000007.jpg)](https://loopwam.github.io/assets/videos/generation/stack_bowls_episode_000007.mp4)

	Lint Roller

	[![Lint Roller, observed](https://loopwam.github.io/assets/posters/generation/lint_roller_remove_dirt_episode_000004.jpg)](https://loopwam.github.io/assets/videos/generation/lint_roller_remove_dirt_episode_000004.mp4)
	[![Lint Roller, predicted](https://loopwam.github.io/assets/posters/generation/lint_roller_remove_dirt_episode_000006.jpg)](https://loopwam.github.io/assets/videos/generation/lint_roller_remove_dirt_episode_000006.mp4)

	## Checkpoints

	Each directory contains one weight file and its `dataset_stats.json`.

	\| Benchmark \| Directory \| Checkpoint \|
	\| --- \| --- \| --- \|
	\| Table30-V2, UR5 \| `table30v2_ur5/` \| `step_029925.pt` \|
	\| Table30-V2, ALOHA \| `table30v2_aloha/` \| `step_061320.pt` \|
	\| Table30-V2, ARX5 \| `table30v2_arx5/` \| `step_057355.pt` \|
	\| Table30-V2, DOS-W1 \| `table30v2_w1/` \| `step_072970.pt` \|
	\| RoboTwin 2.0 \| `robotwin/` \| `step_037645.pt` \|