论文标题
部分可观测时空混沌系统的无模型预测
Representation, learning, and planning algorithms for geometric task and motion planning
论文作者
论文摘要
我们提出了一个学习指导几何任务和运动计划（GTAMP）的框架。 GTAMP是任务和运动计划的一个子类，其中的目标是将多个对象移动到可移动障碍之间的目标区域。标准的图形搜索算法不是直接适用的，因为GTAMP问题涉及混合搜索空间和昂贵的操作可行性检查。为了解决这个问题，我们介绍了一个新颖的计划者，该计划者通过随机抽样和启发式功能扩展基本的启发式搜索，该启发式功能优先考虑可行性，以检查有希望的状态动作对。这种纯计划者的主要缺点是，他们缺乏从计划经验中学习以提高效率的能力。我们提出了两种学习算法来解决这个问题。第一个是用于学习指导离散任务级搜索的等级函数的算法，第二个是学习指导连续运动搜索的采样器的算法。我们提出设计原理，用于设计有效的数据有效概括的数据有效算法，以从计划经验和表示形式中学习。我们在挑战GTAMP问题方面评估了我们的框架，并表明我们可以提高计划效率和数据效率
We present a framework for learning to guide geometric task and motion planning (GTAMP). GTAMP is a subclass of task and motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because GTAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task level search, and the second is an algorithm for learning a sampler that guides the continuous motionlevel search. We propose design principles for designing data efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging GTAMP problems, and show that we can improve both planning and data efficiency
