---
license: apache-2.0
language:
- en
pipeline_tag: robotics
library_name: molmobot
base_model: allenai/MolmoBot-DROID
datasets:
- ccwatson/directional_pick_place_simpledesk_diverse_targets_tested40_200demos_20260716_molmobot
tags:
- molmobot
- robotics
- manipulation
- franka
- droid
- pick-and-place
- spatial-reasoning
- left-right
- molmospaces
- 10000-steps
model-index:
- name: MolmoBot-DROID Directional Pick/Place SimpleDesk Step 10000
  results:
  - task:
      type: robotics
      name: Franka pick-and-place relative left/right
    dataset:
      type: ccwatson/FrankaPickAndPlaceRelativeNextToSimpleDeskDiverseTargetsTested40Bench
      name: MolmoSpaces SimpleDesk Diverse Targets Tested40
      split: 40 benchmark episodes
    metrics:
    - type: success_rate
      name: Strict success rate
      value: 0.625
    - type: lax_success_rate
      name: Lax success rate
      value: 0.65
---

# MolmoBot-DROID Directional Pick/Place SimpleDesk — Step 10,000

This repository contains the **10,000-step intermediate checkpoint** from the
`lr_custom_16k` MolmoBot finetuning run. It was finetuned from
[`allenai/MolmoBot-DROID`](https://huggingface.co/allenai/MolmoBot-DROID) for
Franka/FR3 tabletop pick-and-place commands that require placing one object to
the **left or right of another object**.

The root `config.yaml` and `model.pt` form the unsharded MolmoBot checkpoint
expected by the MolmoBot server. This is not the final 16,000-step checkpoint;
the step is deliberately included in both the repository name and this model
card.

## Checkpoint identity

| Field | Value |
| --- | --- |
| Checkpoint step | **10,000** |
| Training run | `lr_custom_16k` |
| Checkpoint format | MolmoBot unsharded (`config.yaml` + `model.pt`) |
| Base model | `allenai/MolmoBot-DROID` |
| Base model revision | `cbe6ec358958d07ddfb20d3aa54e560e9e1b18c9` |
| Training W&B run | [`ccwatson/molmobot_finetune/ouuzdjlg`](https://wandb.ai/ccwatson/molmobot_finetune/runs/ouuzdjlg) |
| Training Slurm job | `7107997` (`lr-custom-ie-fe-16k-b16-32`) |
| Checkpoint created | 2026-07-17 12:11 UTC |
| `model.pt` size | 19,992,166,548 bytes |
| `model.pt` SHA-256 | `027daf40cbf87cfebc4dcee0aa16b0d6c0a87c2bfb30229ed77f9c1545d600da` |
| `config.yaml` SHA-256 | `7eeb51134b93fc17955f3bd850240074d6b9faf3dcf527177861940c7c098400` |

The complete resolved model/trainer configuration is in `config.yaml`. The
submission-time environment is preserved as `metadata/training_launch.env`, and
machine-readable provenance is in `metadata/provenance.json`.

## Training data

Training used
[`ccwatson/directional_pick_place_simpledesk_diverse_targets_tested40_200demos_20260716_molmobot`](https://huggingface.co/datasets/ccwatson/directional_pick_place_simpledesk_diverse_targets_tested40_200demos_20260716_molmobot)
at dataset revision `5446191605f7eb3020983d83f707153d9719ecfb`.

The dataset contains 200 successful scripted trajectories derived from 40
valid benchmark episodes, with five attempts per episode:

| Split | Source episodes | Trajectories | HDF5 files | Videos |
| --- | ---: | ---: | ---: | ---: |
| Train | 36 | 180 | 180 | 360 |
| Validation | 4 | 20 | 20 | 40 |

The two cameras are `droid_shoulder_light_randomization` and
`wrist_camera_zed_mini`. Prompts were **not randomized** during training.
Normalization was computed from the train split using action quantiles and
state min/max statistics for the eight-dimensional Franka action and state
vectors. The exact statistics are included as `normalization_stats.yaml`
(SHA-256 `972a88e10f02976ce5a4d1c90a2070a62e4daa08a5fe1cecb4ca00e8216eb15d`).

## Main training settings

| Setting | Value |
| --- | --- |
| Intended run length | 16,000 steps |
| This checkpoint | 10,000 steps |
| Device / global batch size | 16 / 32 |
| Sequence length | 928 |
| Observation frames | 2 |
| Observation step delta | 8 |
| Maximum images | 4 |
| Maximum frames | 1 |
| Single-frame preprocessing | false |
| Action preset | `franka_joint` |
| Camera preset | `franka_droid` |
| Action horizon in data | 16 × 8 |
| Weighted sampling | enabled |
| Prompt randomization | disabled |
| Image augmentation | enabled |
| LLM finetuning | enabled |
| Embedding finetuning mode | `ae` |
| Flow timestamps | 8 |
| Connector learning rate | `5e-6` |
| Vision encoder learning rate | `5e-6` |
| LLM learning rate | `1e-5` |
| Action expert learning rate | `1e-4` |
| Validation interval / max examples | 200 / 200 |
| Save and external-eval interval | 2,000 steps |
| Trainer and optimizer state | reset from base model |
| Hardware | 1× NVIDIA B200, 28 CPUs, 224 GiB host memory |

## Closed-loop evaluation

This checkpoint was evaluated on all 40 episodes of
`FrankaPickAndPlaceRelativeNextToSimpleDeskDiverseTargetsTested40Bench` (20
left and 20 right tasks) through the VLA evaluation harness and MolmoSpaces.
The evaluation used natural-language prompts, Filament rendering, 10 parallel
shards, a 500-step task horizon, and both DROID cameras.

Server action settings were:

- execution horizon: 8
- state mode: `cross_attn`
- maximum joint delta: 0.2

| Metric | Result |
| --- | ---: |
| Strict success | **25 / 40 (62.5%)** |
| Lax success | 26 / 40 (65.0%) |
| Strict oracle-done rate | 25 / 40 (62.5%) |
| Lax oracle-done rate | 27 / 40 (67.5%) |
| Average episode length | 274.675 steps |
| Average successful episode length | 140.08 steps |
| Min / max episode length | 32 / 499 steps |
| Failed shards | 0 |
| Missing / duplicate / unexpected episodes | 0 / 0 / 0 |

The eval completed successfully (`eval_returncode=0`) in 2,016.92 seconds.
See the [evaluation W&B run](https://wandb.ai/ccwatson/mlspaces-json-eval/runs/external-eval-lr_custom_16k-step10000),
`metadata/external_eval_request.json`, and
`metadata/external_eval_result.json` for the complete recorded configuration
and result.

## Repository files

- `model.pt`: unsharded MolmoBot weights.
- `config.yaml`: complete resolved model and trainer configuration.
- `normalization_stats.yaml`: exact state/action normalization statistics.
- `metadata/training_launch.env`: exact experiment launch configuration.
- `metadata/external_eval_request.json`: checkpoint identity and requested eval settings.
- `metadata/external_eval_result.json`: complete eval result and coverage accounting.
- `metadata/provenance.json`: source revisions, hashes, run identity, and artifact inventory.

## Intended use and limitations

This checkpoint is intended for research evaluation of the matching
Franka/FR3 DROID-camera setup and directional SimpleDesk tasks. It should be
served with the MolmoBot inference stack and the included normalization stats.
It has only been measured on the 40-episode simulated benchmark described
above. The 62.5% result is a single benchmark evaluation, not a claim of broad
robotic capability or real-world safety. Validate action scaling, cameras,
workspace limits, collision handling, and emergency-stop behavior before any
physical-robot use.

## License

This derivative checkpoint follows the Apache-2.0 license declared by the base
`allenai/MolmoBot-DROID` model. Users remain responsible for the terms of the
base model, training dataset, simulator assets, and other dependencies.