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Multi-policy robot approaches typically train a separate policy for each task, then switch between them via a high-level scheduler based on terrain identification results.
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10/26/2026
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Unverified在方块世界中训练出的规划、导航、多步骤任务等抽象高层次能力比低层运动控制更容易跨域迁移到现实机器人和具身智能场景75% similarUnverified传统机器人控制依赖强化学习或预设程序，需针对每个具体任务单独训练，泛化能力极差74% similarUnverifiedAuxiliary tasks are a common design pattern in multi-task learning, where additional prediction objectives are added beyond the primary training objective, with these auxiliary objectives sharing some of the model's parameters.73% similarUnverifiedA generalist policy refers to a robot policy model pretrained on multiple tasks and scenarios that can be transferred to new instructions, usually based on a vision-language-action (VLA) architecture; existing generalist policies often can only achieve per-task fine-tuning after pretraining, and true zero-shot generalization remains an open problem.72% similarUnverifiedIn the humanoid robotics field, a locomotion policy usually refers to a neural network model trained via reinforcement learning or imitation learning, which takes sensor inputs such as joint angles, IMU orientation, and foot contact forces, and outputs target positions or torques for each joint.71% similar
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