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.
在训练期间限制梯度的幅度，以防止爆炸梯度导致优化不稳定。梯度范数被裁剪到最大值（通常为 1.0–10.0）。梯度裁剪是训练 Transformer、RNN 和强化学习策略的标准做法，其中奖励方差可能导致大的梯度尖峰。
