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 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 |
| 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
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,
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.
