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smolvla_odin_bimanual_handover_450_eps
Mimic-Robotics/smolvla_odin_bimanual_handover_450_eps
smolvla_odin_bimanual_handover_450_eps is a robotics model from Mimic-Robotics. 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 lerobot. The card lists the license as apache-2.0.
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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Repo makeup
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.safetensors1.2 GB · 100%
Parameter types
How the weights are stored.
BF16301M · 67%
Base models
Datasets
- Mimic-Robotics/bimanual_blue_block_handover_1
- Mimic-Robotics/bimanual_blue_block_handover_2
- Mimic-Robotics/bimanual_blue_block_handover_3
- Mimic-Robotics/bimanual_blue_block_handover_4
- Mimic-Robotics/bimanual_blue_block_handover_5
- Mimic-Robotics/bimanual_blue_block_handover_6
- Mimic-Robotics/bimanual_blue_block_handover_7
- Mimic-Robotics/bimanual_blue_block_handover_14
Model card
From the Hugging Face model README
Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.
Evaluate the policy/run inference
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
Model Details
- License: apache-2.0
- Task
- Robotics
- Library
- lerobot
- License
- apache-2.0
- Created
- Nov 19, 2025
- Updated
- Nov 19, 2025