Learning task outcome prediction for robot control from interactive environments
Learning task outcome prediction for robot control from interactive environments
复制标题
从交互式环境中预测机器人控制的学习任务结果
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Beetz
中科院分区：
文献类型：
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作者：
Andrei Haidu;Daniel Kohlsdorf;M. Beetz
为了管理烹饪等复杂任务，未来的机器人需要具有动作感知能力，并具备常识知识。例如，翻转煎饼需要机器人知道锅铲必须在煎饼下面才能成功。我们提出了一种新的方法提取和学习的行动和常识知识，并开发了一个游戏，使用机器人模拟器与现实的物理数据采集。游戏环境是一个虚拟的厨房，其中用户必须通过将煎饼混合物倒在烤箱上并使用刮刀翻转它来创建煎饼。交互是通过用3D输入传感器控制虚拟机器人手来完成的。我们结合了一个现实的流体模拟，以收集适当的数据的浇注行动。此外，我们提出了一个任务结果预测算法，这个特定的系统，并展示了如何学习失败模型的倾倒和翻转动作。
In order to manage complex tasks such as cooking, future robots need to be action-aware and posses common sense knowledge. For example flipping a pancake requires a robot to know that a spatula has to be under a pancake in order to succeed. We present a novel approach for the extraction and learning of action and common sense knowledge, and developed a game using a robot-simulator with realistic physics for data acquisition. The game environment is a virtual kitchen, in which a user has to create a pancake by pouring pancake-mix on an oven and flipping it using a spatula. The interaction is done by controlling a virtual robot hand with a 3D input sensor. We incorporate a realistic fluid simulation in order to gather appropriate data of the pouring action. Furthermore, we present a task outcome prediction algorithm for this specific system and show how to learn a failure model for the pouring and flipping action.