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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 40
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查看详情 ↗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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查看详情 ↗fastwam_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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗lingbot_vla_v2_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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查看详情 ↗lingbot_vla_v2_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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗pi0_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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查看详情 ↗pi0_libero_finetuned_v044
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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查看详情 ↗pi0_old
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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查看详情 ↗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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查看详情 ↗pi05_droid
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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查看详情 ↗pi05-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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗pi05_robocasa
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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查看详情 ↗pi0fast-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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。