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mesa-pi0-fast
albertwilcox/mesa-pi0-fast
mesa-pi0-fast is a robotics model from albertwilcox. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for openpi. The card lists the license as apache-2.0.
π0-FAST (autoregressive prediction of FAST-tokenized action chunks on a PaliGemma-3B backbone, pretrained by Physical Intelligence) finetuned on MESA-70 (70 tasks; LeRobot format) for the single-arm Franka Panda setti…
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Updated Oct 3, 2026
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At a glance
- Task
- Robotics
- Library
- openpi
- License
- apache-2.0
- Access
- Public
- Created
- Feb 25, 2026
- Updated
- Oct 3, 2026
- SHA
- a38a688e
Datasets
Model card
From the Hugging Face model README
MESA π0-FAST
π0-FAST (autoregressive prediction of FAST-tokenized action chunks on a PaliGemma-3B backbone, pretrained by Physical Intelligence) finetuned on MESA-70 (70 tasks; LeRobot format) for the single-arm Franka Panda setting of MESA. This is the checkpoint behind the π0-FAST MESA results in the MESA paper.
This is an openpi (JAX/orbax) checkpoint; use it with our fork,
pairlab/openpi-mesa, config pi0_fast_mesa_70.
Model details
| Base model | π0-FAST (gs://openpi-assets/checkpoints/pi0_fast_base) |
| openpi-mesa config | pi0_fast_mesa_70 |
| Training data | albertwilcox/mesa-70-lerobot |
| Training | 50k steps, batch size 128, default openpi optimizer; final checkpoint (step 49999) |
| Cameras | left-shoulder camera (leftshoulder) and wrist camera (robot0_eye_in_hand), 224x224 RGB |
| Proprioception | 8-D: 7 joint positions + gripper width (robot0_joint_pos, robot0_gripper_jaw_width) |
| Actions | 8-D absolute joint-position targets (7 joints + gripper). The model predicts joint positions relative to the current state (gripper absolute); the policy's output transforms convert them back to absolute targets. |
| Action chunk | 20 steps at 20 Hz; the MESA evaluation server executes the first 5 actions before replanning |
| Language | task instruction |
| Normalization stats | assets/mesa/norm_stats.json |
Usage
Install openpi-mesa following its README, then download and serve the checkpoint:
uv run huggingface-cli download albertwilcox/mesa-pi0-fast --local-dir checkpoints/mesa-pi0-fast
uv run scripts/serve_policy.py --port 8001 policy:checkpoint \
--policy.config=pi0_fast_mesa_70 \
--policy.dir=checkpoints/mesa-pi0-fast
From the MESA repository, run the evaluation server against the same port:
uv run scripts/eval_server_parallel.py \
--port 8001 \
--eval-set-name mesa-70 \
--num-rollouts-per-task 50 \
--controller-type joint_pos
Evaluation suites: mesa-70, mesa-spatial, mesa-instance, mesa-composite, mesa-category. See the
MESA documentation for details.
Results
Success rates (%) from the MESA paper (Table 2), 50 rollouts per task:
| Suite | Success rate |
|---|---|
| MESA-ID | 46.5 |
| MESA-Spatial | 53.6 |
| MESA-Instance | 39.1 |
| MESA-Composite | 28.9 |
| MESA-Category | 8.4 |
| Average | 35.3 |
Notes
- The checkpoint contains the model parameters (
params/), the model config (config/), and normalization stats (assets/); optimizer state is not included. - π0-FAST decodes actions autoregressively; inference is slower than the flow-matching models.
License
Released under the Apache 2.0 license. The model is finetuned from openpi weights that build on PaliGemma; use of the weights may also be subject to the Gemma Terms of Use.
Citation
@inproceedings{
wilcox2026mesa,
title={{MESA}: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics},
author={Wilcox, Albert and Chang, Frank and Nguyen, Nhi and Chakraborty, Aishani and Collins, Jeremy A. and Saxena, Vaibhav and Joffe, Benjamin and Karamcheti, Siddharth and Garg, Animesh},
booktitle={10th Annual Conference on Robot Learning},
year={2026},
url={https://openreview.net/forum?id=Br2rXixvyN}
}
- Task
- Robotics
- Library
- openpi
- License
- apache-2.0
- Created
- Feb 25, 2026
- Updated
- Oct 3, 2026