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反向传播
通过从输出到输入逐层应用链式法则，计算损失函数对神经网络参数梯度的算法。反向传播使深度网络的基于梯度的优化成为可能。它需要在前向传播过程中存储中间激活值，这在训练期间主导内存消耗。
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.
通过从输出到输入逐层应用链式法则，计算损失函数对神经网络参数梯度的算法。反向传播使深度网络的基于梯度的优化成为可能。它需要在前向传播过程中存储中间激活值，这在训练期间主导内存消耗。