---
library_name: transformers
base_model: allenai/MolmoAct2-Think
datasets:
  - Jiafei1224/as
tags:
  - molmoact2
  - robotics
  - image-text-to-text
  - lerobot
  - bimanual
  - depth-reasoning
---

# YAMBox2 groceries — MolmoAct2 Think

This is the depth-reasoning MolmoAct2-Think checkpoint fine-tuned on
[`Jiafei1224/as`](https://huggingface.co/datasets/Jiafei1224/as) for a bimanual
YAM robot putting groceries into a container. This repository contains the
10,000-step checkpoint from the 50,000-step training run.

**Adaptive depth is enabled by default.** `config.json` contains
`"enable_depth_reasoning": true`, and calling `predict_action` without an
`enable_depth_reasoning` argument reads that checkpoint default. Pass the
returned `depth_cache` into the next frame to reuse unchanged depth regions and
regenerate changed regions adaptively. No dataset `depth_updated_mask` is
required at inference time.

## Policy contract

- Base checkpoint: `allenai/MolmoAct2-Think`
- Cameras, in order: `observation.images.top`, `observation.images.left`,
  `observation.images.right`
- State: `observation.state`, 14 dimensions
- Output: 14-dimensional absolute joint targets
- Action horizon / returned steps: 30 / 30
- Normalization tag: `yam_dual_molmoact2`
- Grippers are not separately normalized
- Training sequence length: 896
- Depth representation: 10 x 10 discrete bins (100 codes)
- Default output style: `robot_depth_action`

The converted action expert has a padded internal width of 32, but
`norm_stats.json` defines the real robot action width as 14 and `predict_action`
returns only those 14 dimensions.

## Install

```bash
pip install "torch" "transformers==5.14.1" accelerate pillow numpy safetensors
```

The model uses custom Transformers code included in this repository, so loading
requires `trust_remote_code=True`.

## Run with adaptive depth

```python
import numpy as np
import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "hqfang/think"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda").eval()

assert model.config.enable_depth_reasoning is True
depth_cache = None

def predict(top_path, left_path, right_path, state):
    global depth_cache
    # Keep this exact camera order. Adaptive change detection uses the first image.
    images = [
        Image.open(top_path).convert("RGB"),
        Image.open(left_path).convert("RGB"),
        Image.open(right_path).convert("RGB"),
    ]
    with torch.inference_mode():
        result = model.predict_action(
            processor=processor,
            images=images,
            task="put the groceries into the container",
            state=np.asarray(state, dtype=np.float32),
            norm_tag="yam_dual_molmoact2",
            expected_action_representation="absolute",
            inference_action_mode="continuous",
            num_steps=10,
            n_action_steps=30,
            depth_cache=depth_cache,
            # No enable_depth_reasoning argument: the checkpoint default is True.
        )
    depth_cache = result.depth_cache
    actions = result.actions[0].float().cpu().numpy()
    assert actions.shape == (30, 14)
    assert result.depth_bins.shape[-1] == 100
    return actions

actions = predict("top.png", "left.png", "right.png", [0.0] * 14)
```

The first frame generates the complete depth representation. On later frames,
passing `depth_cache` enables adaptive selective regeneration. To explicitly
disable depth for an ablation, pass `enable_depth_reasoning=False` and omit the
cache.

`actions[t]` is an absolute 14-dimensional joint target, not a delta. Validate
joint ordering, limits, timing, and emergency-stop behavior before commanding
physical hardware.

## Open-loop check

The fixed-random open-loop evaluation used episode 2 from `Jiafei1224/as`
(1,488 frames, continuous inference, adaptive depth, 10 flow steps). Raw action
MSE was `0.0012067767` at step 10,000. This is an offline reconstruction metric,
not a robot task-success measurement.

## Citation

```bibtex
@misc{fang2026molmoact2actionreasoningmodels,
  title={MolmoAct2: Action Reasoning Models for Real-world Deployment},
  author={Haoquan Fang and Jiafei Duan and Donovan Clay and Sam Wang and Shuo Liu and Weikai Huang and Xiang Fan and Wei-Chuan Tsai and Shirui Chen and Yi Ru Wang and Shanli Xing and Jaemin Cho and Jae Sung Park and Ainaz Eftekhar and Peter Sushko and Karen Farley and Angad Wadhwa and Cole Harrison and Winson Han and Ying-Chun Lee and Eli VanderBilt and Rose Hendrix and Suveen Ellawela and Lucas Ngoo and Joyce Chai and Zhongzheng Ren and Ali Farhadi and Dieter Fox and Ranjay Krishna},
  year={2026},
  eprint={2605.02881},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}
```