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.
从专家演示中学习奖励函数,假设专家(大约)最优地最大化该奖励。逆强化学习避免了手工设计奖励函数的需要——相反,奖励被推断出来,然后用于通过标准强化学习训练策略。最大熵IRL和对抗IRL(AIRL)是流行的公式。逆强化学习与GAIL密切相关。