Introducing a Human-like Planner for Reaching in Cluttered Environments
Introducing a Human-like Planner for Reaching in Cluttered Environments
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发表时间：
2020-02
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通讯作者：
Mohamed Hasan;Matthew Warburton;Wisdom C. Agboh;M. Dogar;M. Leonetti;He Wang;F. Mushtaq;M. Mon-Williams;A. Cohn
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作者：
Mohamed Hasan;Matthew Warburton;Wisdom C. Agboh;M. Dogar;M. Leonetti;He Wang;F. Mushtaq;M. Mon-Williams;A. Cohn
与机器人相比，人类非常擅长在杂乱的环境中拿取物体。现有最好的机器人规划器基于配置空间中的随机采样，当对象数量较多时，配置空间的维度变得过高。因此，大多数规划者都受到有限的对象操作的困扰。我们通过向人类学习高级操作规划技能并将这些技能转移给机器人规划师来解决这个问题。当人类参与者伸手去拿杂乱桌面上的物体时，我们使用虚拟现实来生成数据。由此，我们设计了任务空间的定性表示，以抽象人类决策，而不管途中的物体数量如何。基于这种表示，对人类演示进行分段并用于训练决策分类器。使用这些分类器，我们的规划器在任务空间中生成了一个路径点列表。这些路径点提供了一个高级计划，可以将其转移到任意机器人模型并用于初始化本地轨迹优化器。我们通过对看不见的人类 VR 数据进行测试、基于物理的机器人模拟和真实的机器人实验来评估这种方法。我们发现，这种类人规划器的性能优于最先进的标准轨迹优化算法，并且能够生成有效的快速规划策略，而不管杂乱环境中的对象数量如何。我们的数据集和源代码是公开的。
Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling in configuration space -- which becomes excessively high-dimensional with a large number of objects. Consequently, most of these planners suffer from limited object manipulation. We address this problem by learning high-level manipulation planning skills from humans and transfer these skills to robot planners. We used virtual reality to generate data from human participants whilst they reached for objects on a cluttered table top. From this, we devised a qualitative representation of the task space to abstract human decisions, irrespective of the number of objects in the way. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provide a high-level plan, which can be transferred to an arbitrary robot model and used to initialize a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation and real robot experiments. We find that this human-like planner outperforms a state-of-the-art standard trajectory optimisation algorithm and is able to generate effective strategies for rapid planning, irrespective of the number of objects in a cluttered environment. Our dataset and source code are publicly available.