π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…
Open-weight model · Robotics
bento_ur7e_pi05_v5_dagger
by Polaris.AI Robotics polarisai-robots/bento_ur7e_pi05_v5_dagger
bento_ur7e_pi05_v5_dagger is an open-weight model for robotics from Polaris.AI Robotics, released under Apache License 2.0. It has 4.1B parameters. At 16-bit it needs about 9.9 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training.
Parameters4.1B
Context—
Weights9.4 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve bento_ur7e_pi05_v5_dagger (4.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 8.3 GB | 9.9 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 4.1 GB | 5.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 2.1 GB | 2.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
bento_ur7e_pi05_v5_dagger on every accelerator the SAVRN Index prices, at every precision
Model Card
By Polaris.AI Robotics, published under apache-2.0, revision e21d2802a4d0.
π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…
Read Polaris.AI Robotics's full model card
Model Card for pi05
π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository.
This policy has been trained and pushed to the Hub using LeRobot.
Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation.
Model Details
- License: apache-2.0
- Fine-tuned from: lerobot/pi05_base
- Robot type:
ur7e - Cameras:
top,wrist
Inputs & Outputs
The policy consumes these observation features and produces these action features.
Inputs
| Feature | Type | Shape |
|---|---|---|
observation.images.base_0_rgb |
VISUAL | (3, 224, 224) |
observation.images.left_wrist_0_rgb |
VISUAL | (3, 224, 224) |
observation.images.right_wrist_0_rgb |
VISUAL | (3, 224, 224) |
observation.state |
STATE | (32,) |
Outputs
| Feature | Type | Shape |
|---|---|---|
action |
ACTION | (7,) |
Training Dataset
- Repository: polarisai-robots/bento_ur7e_v3
- Episodes: 449
- Frames: 849750
- Frame rate: 60 FPS
- Task(s): "Pack me a lunchbox with two fried chicken pieces and two brocolli.", "Pack me a lunchbox with two fish fillet and three sausages."
Training Configuration
| Setting | Value |
|---|---|
| Training steps | 30000 |
| Batch size | 32 |
| Optimizer | adamw |
| Learning rate | 5e-05 |
| Seed | 1000 |
| LeRobot version | 0.6.0 |
How to Get Started with the Model
New to LeRobot? These guides cover the full workflow:
- Install LeRobot — set up the
lerobotpackage. - Hardware setup — assemble, wire, and calibrate your robot and cameras.
- Record data & train a policy — the end-to-end imitation-learning walkthrough.
- CLI cheat-sheet — quick reference for the
lerobot-*commands.
The short version to run and train this policy:
Run the policy on your robot
lerobot-rollout \
--strategy.type=base \
--robot.type=ur7e \
--robot.port=<your_robot_port> \
--robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
--policy.path=polarisai-robots/bento_ur7e_pi05_v5_dagger \
--task="Pack me a lunchbox with two fried chicken pieces and two brocolli." \
--duration=60
Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.
When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.
Train your own policy
This policy type is usually fine-tuned from the pretrained base model lerobot/pi05_base:
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.path=lerobot/pi05_base \
--output_dir=outputs/train/<policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<policy_repo_id> \
--wandb.enable=true
Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.
Evaluation
No evaluation results have been provided for this policy yet.
Citation
If you use this policy, please cite the method linked in the description above, along with LeRobot:
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
howpublished = "\url{https://github.com/huggingface/lerobot}",
year = {2024}
}
Identity and Version
- Repository
- polarisai-robots/bento_ur7e_pi05_v5_dagger
- Publisher
- Polaris.AI Robotics
- Task
- Robotics
- Modality
- Control
- Library
- lerobot
- Parameters
- 4.1B parameters
- Languages
- Not stated by the source
- Revision
- e21d2802a4d040b8e1236eaed531591f77baa7ec
- First published
- 2026-10-17
- Last updated
- 2026-10-17
Files and Weights
9 files, 9.4 GB in total. The weights are 3 files totalling 9.4 GB in safetensors.
Weights3 files · 9.4 GB
Configuration4 files · 13.4 KB
Documentation1 file · 6.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 9.4 GB | 5506d4a3303f |
| policy_postprocessor_step_0_unnormalizer_processor.safetensors | Weights | 7.6 KB | b9c51bcada4b |
| policy_preprocessor_step_3_normalizer_processor.safetensors | Weights | 7.6 KB | b9c51bcada4b |
| config.json | Configuration | 2.7 KB | — |
| policy_postprocessor.json | Configuration | 779 B | — |
| policy_preprocessor.json | Configuration | 2.3 KB | — |
| train_config.json | Configuration | 7.6 KB | — |
| README.md | Documentation | 6.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 9.4 GB
Download from Polaris.AI Robotics
Released by Polaris.AI Robotics through its official repository on Hugging Face. Read the license.
Built From
- Derived from lerobot/pi05_base
- Trained on (disclosed) polarisai-robots/bento_ur7e_v3
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 9.4 GB |
| 16-bit | 8.3 GB |
| 8-bit | 4.1 GB |
| 4-bit | 2.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bento_ur7e_pi05_v5_dagger
How much GPU memory does bento_ur7e_pi05_v5_dagger need?
About 9.9 GB at 16-bit and 2.5 GB at 4-bit: the weights (4.1B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run bento_ur7e_pi05_v5_dagger on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use bento_ur7e_pi05_v5_dagger commercially?
Yes. bento_ur7e_pi05_v5_dagger is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…
π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…