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.
隐式Q学习——一种离线强化学习算法，通过使用期望值回归目标来避免在分布外动作上查询Q函数。IQL在没有CQL显式保守性的情况下实现了强大的离线强化学习性能，使其更易于实现和调整。它已成为在演示数据上训练的机器人操纵任务的流行方法。
