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行为正则化
一类离线强化学习方法，通过约束学习策略与收集数据的行为策略保持接近，防止对分布外动作的利用。方法包括：策略约束（TD3+BC）、KL散度惩罚和支撑约束（BEAR）。行为正则化是实现稳定离线强化学习的关键机制。
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.
一类离线强化学习方法，通过约束学习策略与收集数据的行为策略保持接近，防止对分布外动作的利用。方法包括：策略约束（TD3+BC）、KL散度惩罚和支撑约束（BEAR）。行为正则化是实现稳定离线强化学习的关键机制。