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损失函数
量化模型预测与真实标签之间差异的数学函数。损失在训练期间被最小化。机器人学习中的常见损失：MSE（连续动作）、交叉熵（离散动作）、扩散损失（去噪分数匹配）和对比损失（表示学习）。损失函数的选择直接影响模型学到的内容。
Open 7-DoF arm, bimanual-ready. Ships from San Francisco with pilot data-collection packages from $2,500.
90+ platforms — humanoids, arms, quadrupeds, dexterous hands, and teleoperation kits. In-stock hardware ships in 48 hours.
Hardware-synced demonstrations for VLA and imitation learning. Download in LeRobot, HDF5, or RLDS format.
From first arm on the bench to a policy that survives a real workcell — the Robotics Center pipeline in three steps.
Low-latency data-collection glove for dexterous manipulation. From $5,500, ships from San Francisco.
Unbox, calibrate, and run your first autonomous walk. Includes ROS 2 bringup and teleop quickstart.
量化模型预测与真实标签之间差异的数学函数。损失在训练期间被最小化。机器人学习中的常见损失：MSE（连续动作）、交叉熵（离散动作）、扩散损失（去噪分数匹配）和对比损失（表示学习）。损失函数的选择直接影响模型学到的内容。