Human-like Planning for Reaching in Cluttered Environments
Human-like Planning for Reaching in Cluttered Environments
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DOI：
10.1109/icra40945.2020.9196665
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发表时间：
2020-01
期刊：
影响因子：
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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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文献类型：
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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伸手去拿东西。现有最好的机器人规划是基于构型空间的随机抽样，而这在大量对象的情况下会变得过于高维。因此，在这样的环境中，大多数规划人员往往无法有效地找到对象操作计划。我们通过识别人类的高级操作计划来解决这个问题，并将这些技能转移到机器人计划者身上。我们使用虚拟现实技术来捕捉人类参与者在布满障碍物的桌面上触摸目标物体的过程。由此，我们设计了一个任务空间的定性表示来抽象决策，而不考虑障碍的数量。基于这种表示，人类演示被分割并用于训练决策分类器。使用这些分类器，我们的计划器在任务空间中生成了一个路点列表。这些路径点提供了一个高级计划，可以转移到任意机器人模型，并用于初始化局部轨迹优化器。我们通过对未见过的人类VR数据、基于物理的机器人模拟和真实机器人进行测试来评估这种方法（数据集和代码是公开的1）。我们发现，类似人类的规划器优于最先进的标准轨迹优化算法，并且能够为快速规划生成有效的策略-无论环境中障碍物的数量如何。
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 of configuration space- which becomes excessively high-dimensional with large number of objects. Consequently, most planners often fail to efficiently find object manipulation plans in such environments. We addressed this problem by identifying high-level manipulation plans in humans, and transferring these skills to robot planners. We used virtual reality to capture human participants reaching for a target object on a tabletop cluttered with obstacles. From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles. 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 provided a high-level plan, which could be transferred to an arbitrary robot model and used to initialise a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation, and a real robot (dataset and code are publicly available1). We found that the human-like planner outperformed a state-of-the-art standard trajectory optimisation algorithm, and was able to generate effective strategies for rapid planning- irrespective of the number of obstacles in the environment.