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.
约束强化学习，在满足安全约束（如碰撞概率<0.01、关节扭矩限制）的同时优化回报。约束策略优化（CPO）、拉格朗日方法和控制屏障函数是常见方法。安全强化学习对于实际机器人部署至关重要，因为约束违反可能损坏硬件或伤害人员。
