NVIDIA Releases Cosmos3-Nano Policy Trained on LIBERO-10
nvidia/cosmos3-nano-libero10-sdg-export-R
NVIDIA has released a policy model trained on LIBERO-10, part of the Cosmos Cookbook recipe, with performance metrics showing 93.9% success in standard scenes and 85.1% in perturbed scenes. The model is available under the NVIDIA Open Model License.
NVIDIA provides an open-source policy model trained on LIBERO-10, with performance metrics reported across standard and perturbed scenes, highlighting the model's adaptability to varied conditions.
Source: NVIDIA — Robotics model repository updates · Read original article ↗
Article text · Original source · English
Cosmos3-Nano LIBERO-10 policy, cell R
A Cosmos3-Nano action policy post-trained on LIBERO-10, one cell of the controlled synthetic-data study in the Cosmos Cookbook recipe "Restyle LIBERO-10 Demonstrations with Cosmos3-Nano-Sim-Transfer and Measure the Gain Against a Matched Control". Export of the training checkpoint (bf16 safetensors, EMA weights, 7 shards, about 30 GB); loadable by the cosmos-framework policy server. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3, and the restyled sets are regenerated by the recipe.
| Training data | 379 real LIBERO-10 demonstrations |
| Iterations | 0->2000 (global batch 2048, training seed 42) |
| Success, standard scenes | 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) |
| Success, perturbed scenes | 85.1 +/- 0.6 % (same initial states with randomised lighting, textures, skybox and camera jitter) |
| Base model | nvidia/Cosmos3-Nano, revision e59a53c2 |
| Framework | cosmos-framework c23e51f2 (v1.2.2) |
Read the recipe before comparing cells: a restyled cell is only interpretable next to its duplicate control, and differences under 2.5 points are within the training-seed spread.
Licence: derivative of nvidia/Cosmos3-Nano; the NVIDIA Open Model License applies.
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
number of real LIBERO-10 demonstrations
379
View original evidence
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3number of tasks
10
View original evidence
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3number of initial states
50
View original evidence
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3number of evaluation seeds
3
View original evidence
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3number of training iterations
>2,000
View original evidence
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3number of training shards
7
View original evidence
Cosmos3-Nano LIBERO-10 policy, cell R A Cosmos3-Nano action policy post-trained on LIBERO-10, one cell of the controlled synthetic-data study in the Cosmos Cookbook recipe "Restyle LIBERO-10 Demonstrations with Cosmos3-Nano-Sim-Transfer and Measure the Gain Against a Matched Control". Export of the training checkpoint (bf16 safetensors, EMA weights, 7 shards, about 30 GB); loadable by the cosmos-framework policy serv
Open source S2
- code: not_reported
- weights: not_reported
- dataset: not_reported
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
er. The training data is not redistributed here: LIBERO-10 is at nvidia/LIBERO_LeRobot_v3 , and the restyled sets are regenerated by the recipe. Training data 379 real LIBERO-10 demonstrations Iterations 0->2000 (global batch 2048, training seed 42) Success, standard scenes 93.9 +/- 1.0 % (10 tasks x 50 initial states, three evaluation seeds) Success, perturbed scenes 85.1 +/- 0.6 % (same initial states with randomis
Open source S3
Cosmos3-Nano LIBERO-10 policy, cell R A Cosmos3-Nano action policy post-trained on LIBERO-10, one cell of the controlled synthetic-data study in the Cosmos Cookbook recipe "Restyle LIBERO-10 Demonstrations with Cosmos3-Nano-Sim-Transfer and Measure the Gain Against a Matched Control". Export of the training checkpoint (bf16 safetensors, EMA weights, 7 shards, about 30 GB); loadable by the cosmos-framework policy serv
Open source S2
Source：NVIDIA — Robotics model repository updates · huggingface.co