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L2 正则化
在损失函数中添加与模型权重平方幅度成正比的惩罚项，抑制大权重并减少过拟合。L2 正则化（权重衰减）应用于几乎所有神经网络训练。在机器人学习中，适当的权重衰减可以防止控制策略记忆特定的演示轨迹。
Open 7-DoF arm, bimanual-ready, with pilot data-collection packages.
90+ platforms — humanoids, arms, quadrupeds, dexterous hands, and teleoperation kits. Lead time shown on each product.
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.
Specifications and integration guide for the tactile and pose-tracking data glove.
Unbox, calibrate, and run your first autonomous walk. Includes ROS 2 bringup and teleop quickstart.
在损失函数中添加与模型权重平方幅度成正比的惩罚项，抑制大权重并减少过拟合。L2 正则化（权重衰减）应用于几乎所有神经网络训练。在机器人学习中，适当的权重衰减可以防止控制策略记忆特定的演示轨迹。