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元学习
学会学习——在任务分布上训练模型，使其能够以最少数据快速适应新任务。在机器人学习中，MAML、ProMP和任务条件策略等元学习方法实现少样本适应新物体、环境或任务变化。模型学习一个初始化或适应策略，在任务分布上广泛有效。
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.
学会学习——在任务分布上训练模型，使其能够以最少数据快速适应新任务。在机器人学习中，MAML、ProMP和任务条件策略等元学习方法实现少样本适应新物体、环境或任务变化。模型学习一个初始化或适应策略，在任务分布上广泛有效。