Robots need robust models to effectively perform tasks that humans do on a daily basis. These models often require substantial developmental costs to maintain because they need to be adjusted and adapted over time. Deep reinforcement learning is a powerful approach for acquiring complex real-world models because there is no need for a human to design the model manually. Furthermore, a robot can establish new motions and optimal trajectories that may not have been considered by a human. However, the cost of learning is an issue because it requires a huge amount of trial and error in the real world. Here, we report a method for realizing complicated tasks in the real world with low design and teaching costs based on the principle of prediction error minimization. We devised a module integration method by introducing a mechanism that switches modules based on the prediction error of multiple modules. The robot generates appropriate motions according to the door’s position, color, and pattern with a low teaching cost. We also show that by calculating the prediction error of each module in real time, it is possible to execute a sequence of tasks (opening door outward and passing through) by linking multiple modules and responding to sudden changes in the situation and operating procedures. The experimental results show that the method is effective at enabling a robot to operate autonomously in the real world in response to changes in the environment.

中文翻译：

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高效的多任务学习，带有全身控制的开门和进入预测模型

机器人需要强大的模型来有效地执行人类每天所做的任务。这些模型通常需要大量的开发成本来维护，因为它们需要随着时间的推移进行调整和调整。深度强化学习是获取复杂现实世界模型的强大方法，因为不需要人工手动设计模型。此外，机器人可以建立人类可能没有考虑过的新运动和最佳轨迹。然而，学习成本是一个问题，因为它需要在现实世界中进行大量的试验和错误。在这里，我们报告了一种基于预测误差最小化原则以低设计和教学成本在现实世界中实现复杂任务的方法。我们通过引入一种基于多个模块的预测误差来切换模块的机制，设计了一种模块集成方法。机器人根据门的位置、颜色和图案产生适当的动作，教学成本低。我们还表明，通过实时计算每个模块的预测误差，可以通过链接多个模块并响应情况和操作程序的突然变化来执行一系列任务（向外开门和通过）。实验结果表明，该方法能够有效地使机器人在现实世界中响应环境的变化自主运行。教学成本低的模式。我们还表明，通过实时计算每个模块的预测误差，可以通过链接多个模块并响应情况和操作程序的突然变化来执行一系列任务（向外开门和通过）。实验结果表明，该方法能够有效地使机器人在现实世界中响应环境的变化自主运行。教学成本低的模式。我们还表明，通过实时计算每个模块的预测误差，可以通过链接多个模块并响应情况和操作程序的突然变化来执行一系列任务（向外开门和通过）。实验结果表明，该方法能够有效地使机器人在现实世界中响应环境的变化自主运行。