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.
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。
