We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a
means of efficiently moving scattered objects into a target receptacle. Due to
the chaotic nature of aerodynamic forces, a blowing controller must (i)
continually adapt to unexpected changes from its actions, (ii) maintain
fine-grained control, since the slightest misstep can result in large
unintended consequences (e.g., scatter objects already in a pile), and (iii)
infer long-range plans (e.g., move the robot to strategic blowing locations).
We tackle these challenges in the context of deep reinforcement learning,
introducing a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively
combine high-level planning and low-level closed-loop control for dynamic
mobile manipulation. Experiments show that our system learns efficient
behaviors for the task, demonstrating in particular that blowing achieves
better downstream performance than pushing, and that our policies improve
performance over baselines. Moreover, we show that our system naturally
encourages emergent specialization between the different subpolicies spanning
low-level fine-grained control and high-level planning. On a real mobile robot
equipped with a miniature air blower, we show that our simulation-trained
policies transfer well to a real environment and can generalize to novel
objects.

该研究介绍通过气动非抓取性操纵（吹气）来有效地将散乱的物体移动到目标容器。研究通过基于深度强化学习的空间动作地图框架实现了高水平的规划和低水平的闭环控制的有效组合。结果表明，吹气比推动等方法更有效，也证明了在低水平微观控制和高水平规划等不同子策略之间存在专业化的合作。在实际机器人上，该系统的仿真训练策略成功地转移到了实际环境，并可以泛化到新颖物体上。

移动风机下的气动非抓握操作学习

Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower

Typical end-to-end formulations for learning robotic navigation involve
predicting a small set of steering command actions (e.g., step forward, turn
left, turn right, etc.) from images of the current state (e.g., a bird's-eye
view of a SLAM reconstruction). Instead, we show that it can be advantageous to
learn with dense action representations defined in the same domain as the
state. In this work, we present "spatial action maps," in which the set of
possible actions is represented by a pixel map (aligned with the input image of
the current state), where each pixel represents a local navigational endpoint
at the corresponding scene location. Using ConvNets to infer spatial action
maps from state images, action predictions are thereby spatially anchored on
local visual features in the scene, enabling significantly faster learning of
complex behaviors for mobile manipulation tasks with reinforcement learning. In
our experiments, we task a robot with pushing objects to a goal location, and
find that policies learned with spatial action maps achieve much better
performance than traditional alternatives.

本文研究机器人导航中的动作表示，提出了 “空间动作地图” 概念，使用卷积神经网络从状态图像推断出空间动作地图，从而显著加快了使用强化学习方法实现移动操作任务的复杂行为的学习