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.
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。
