This paper presents a hybrid online Partially Observable Markov Decision
Process (POMDP) planning system that addresses the problem of autonomous
navigation in the presence of multi-modal uncertainty introduced by other
agents in the environment. As a particular example, we consider the problem of
autonomous navigation in dense crowds of pedestrians and among obstacles.
Popular approaches to this problem first generate a path using a complete
planner (e.g., Hybrid A*) with ad-hoc assumptions about uncertainty, then use
online tree-based POMDP solvers to reason about uncertainty with control over a
limited aspect of the problem (i.e. speed along the path). We present a more
capable and responsive real-time approach enabling the POMDP planner to control
more degrees of freedom (e.g., both speed AND heading) to achieve more flexible
and efficient solutions. This modification greatly extends the region of the
state space that the POMDP planner must reason over, significantly increasing
the importance of finding effective roll-out policies within the limited
computational budget that real time control affords. Our key insight is to use
multi-query motion planning techniques (e.g., Probabilistic Roadmaps or Fast
Marching Method) as priors for rapidly generating efficient roll-out policies
for every state that the POMDP planning tree might reach during its limited
horizon search. Our proposed approach generates trajectories that are safe and
significantly more efficient than the previous approach, even in densely
crowded dynamic environments with long planning horizons.

本文提出了一种混合在线部分可观察马尔可夫决策过程规划系统，针对多模态不确定性对自主导航的影响。通过使用多查询运动规划和在线 POMDP 求解器，我们实现了更加灵活和高效的实时方法，从而更好地解决了密集人群和障碍物之间的自主导航问题，并获得了更高的安全性和效率。

带拓展空间 POMDP 规划的人群意图感知导航

Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning

We present an accurate, real-time approach to robotic grasp detection based
on convolutional neural networks. Our network performs single-stage regression
to graspable bounding boxes without using standard sliding window or region
proposal techniques. The model outperforms state-of-the-art approaches by 14
percentage points and runs at 13 frames per second on a GPU. Our network can
simultaneously perform classification so that in a single step it recognizes
the object and finds a good grasp rectangle. A modification to this model
predicts multiple grasps per object by using a locally constrained prediction
mechanism. The locally constrained model performs significantly better,
especially on objects that can be grasped in a variety of ways.

本文介绍了一种基于卷积神经网络的准确、实时的机器人抓取检测方法，该网络可以在单个阶段内执行分类并查找一个优质的抓取矩形，并使用局部约束机制预测每个对象的多重抓取，比现有方法高 14 个百分点性能更好，尤其是对于可以用不同方式抓取的物体。