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.
保守Q学习——一种离线强化学习算法，通过对分布外动作的Q值添加惩罚来学习保守的（悲观的）Q函数。这防止了导致离线强化学习控制策略利用数据不支持的虚假高价值区域的高估问题。CQL是机器人操纵中最广泛使用的离线强化学习方法之一。
