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系统辨识
估计真实机器人或环境的物理参数（质量、摩擦系数、关节阻尼、接触刚度）以提高模拟保真度的过程。准确的系统辨识可减少仿真-现实差距。方法包括对记录轨迹的最小二乘拟合、贝叶斯优化和基于神经网络的辨识。
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.
估计真实机器人或环境的物理参数（质量、摩擦系数、关节阻尼、接触刚度）以提高模拟保真度的过程。准确的系统辨识可减少仿真-现实差距。方法包括对记录轨迹的最小二乘拟合、贝叶斯优化和基于神经网络的辨识。