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View details ↗diffusion_pusht
lerobotDiffusion Policy (as per Diffusion Policy: Visuomotor Policy Learning via Action Diffusion) trained for the `PushT` environment from gym-pusht. See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model. The model was trained using LeRobot's training script and with the pusht dataset, using this command:
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View details ↗fastwam_base
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View details ↗FastWAM is a world action model built on Wan2.2 and an action diffusion expert. It predicts continuous robot action chunks from visual observations, proprioception, and language/task context. Implementation: This LeRobot implementation follows the original reference code for compatibility. Reference implementation: [https://github.com/yuantianyuan01/FastWAM] For full installation details, see the official LeRobot documentation: https://huggingface.co/docs/lerobot/installation
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View details ↗lingbot_va_libero_long
lerobotLingBot-VA is an autoregressive video-action world-model policy built on the Wan2.2 video-diffusion stack. It interleaves the prediction of future video latents and robot actions in a single autoregressive sequence, feeding observed keyframes back into its KV cache for closed-loop world modeling. 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.
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View details ↗lingbot_va_robotwin
lerobot基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lingbot_vla_v2_base
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View details ↗lingbot_vla_v2_robotwin
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View details ↗MolmoAct2-SO100_101-LeRobot
lerobot基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗pi0_base
lerobotπ₀ is a Vision-Language-Action (VLA) foundation model from Physical Intelligence that jointly reasons over vision, language, and actions to control robots, serving as the base architecture that later enabled π₀.₅’s open-world generalization. **Original paper:** π0: A Vision-Language-Action Flow Model for General Robot Controlion **Reference implementation:** https://github.com/Physical-Intelligence/openpi **LeRobot implementation:** Follows the original reference code for compatibility. For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation
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View details ↗pi0_libero_base
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View details ↗pi0_libero_finetuned_v044
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View details ↗pi0_old
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View details ↗pi05_base
lerobotπ₀.₅ is a Vision-Language-Action (VLA) model with open-world generalization from Physical Intelligence, co-trained on robot demonstrations and large-scale multimodal data to execute long-horizon tasks in unseen real-world environments. **Note:** This model currently supports only the flow-matching action head for π₀.₅ training and inference. Other components from the original work (e.g., subtask prediction, action tokenization, or RL) were not released upstream and are not included here, though the LeRobot team is actively working to support them. **Original paper:** π0.5: A Vision-Language-Action Model with Open-World Generalization **Reference implementation:** https://github.com/Physical-Intelligence/openpi **LeRobot implementation:** Follows the original reference code for compatibility.
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View details ↗pi05_droid
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View details ↗pi05-libero
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View details ↗pi05_libero_base
lerobot基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗pi05_libero_finetuned_v044
lerobotπ₀.₅ is a Vision-Language-Action (VLA) model with open-world generalization from Physical Intelligence, co-trained on robot demonstrations and large-scale multimodal data to execute long-horizon tasks in unseen real-world environments. **Note:** This model currently supports only the flow-matching action head for π₀.₅ training and inference. Other components from the original work (e.g., subtask prediction, action tokenization, or RL) were not released upstream and are not included here, though the LeRobot team is actively working to support them. **Original paper:** π0.5: A Vision-Language-Action Model with Open-World Generalization **Reference implementation:** https://github.com/Physical-Intelligence/openpi **LeRobot implementation:** Follows the original reference code for compatibility.
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View details ↗pi05_robocasa
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View details ↗pi0fast-base
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View details ↗pi0fast-libero
lerobot基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗resnet10
lerobotIt's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. - **Original Framework:** JAX - **Target Framework:** PyTorch - **Model Architecture:** ResNet-10 (4-stage ResNet with basic blocks) - **Original Model:** HIL-SERL ResNet-10 - **Total Parameters:** 4,905,792 (~4.9M parameters) - **Hidden Sizes:** [64, 128, 256, 512] - **Input:** 3-channel RGB images (128x128) - **Embedding Size:** 64
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