This article presents a novel telepresence system for advancing aerial
manipulation in dynamic and unstructured environments. The proposed system not
only features a haptic device, but also a virtual reality (VR) interface that
provides real-time 3D displays of the robot's workspace as well as a haptic
guidance to its remotely located operator. To realize this, multiple sensors
namely a LiDAR, cameras and IMUs are utilized. For processing of the acquired
sensory data, pose estimation pipelines are devised for industrial objects of
both known and unknown geometries. We further propose an active learning
pipeline in order to increase the sample efficiency of a pipeline component
that relies on Deep Neural Networks (DNNs) based object detection. All these
algorithms jointly address various challenges encountered during the execution
of perception tasks in industrial scenarios. In the experiments, exhaustive
ablation studies are provided to validate the proposed pipelines.
Methodologically, these results commonly suggest how an awareness of the
algorithms' own failures and uncertainty (`introspection') can be used tackle
the encountered problems. Moreover, outdoor experiments are conducted to
evaluate the effectiveness of the overall system in enhancing aerial
manipulation capabilities. In particular, with flight campaigns over days and
nights, from spring to winter, and with different users and locations, we
demonstrate over 70 robust executions of pick-and-place, force application and
peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a
result, we show the viability of the proposed system in future industrial
applications.

本文提出了一种新型遥控系统，包含了触觉设备以及虚拟现实界面，应用于动态和非结构化环境下的空中机器人操作。利用多个传感器进行感知、姿态估计以及神经网络的主动学习等算法，针对工业领域的任务进行了验证实验，并展示了该系统在未来工业应用中的可行性。

基于物体姿态估计和主动学习的虚拟现实：实现具有空中操纵能力的遥控机器人

Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities

This paper is concerned with learning transferable contact models for aerial
manipulation tasks. We investigate a contact-based approach for enabling
unmanned aerial vehicles with cable-suspended passive grippers to compute the
attach points on novel payloads for aerial transportation. This is the first
time that the problem of autonomously generating contact points for such tasks
has been investigated. Our approach builds on the underpinning idea that we can
learn a probability density of contacts over objects' surfaces from a single
demonstration. We enhance this formulation for encoding aerial transportation
tasks while maintaining the one-shot learning paradigm without handcrafting
task-dependent features or employing ad-hoc heuristics; the only prior is
extrapolated directly from a single demonstration. Our models only rely on the
geometrical properties of the payloads computed from a point cloud, and they
are robust to partial views. The effectiveness of our approach is evaluated in
simulation, in which one or three quadropters are requested to transport
previously unseen payloads along a desired trajectory. The contact points and
the quadroptors configurations are computed on-the-fly for each test by our
apporach and compared with a baseline method, a modified grasp learning
algorithm from the literature. Empirical experiments show that the contacts
generated by our approach yield a better controllability of the payload for a
transportation task. We conclude this paper with a discussion on the strengths
and limitations of the presented idea, and our suggested future research
directions.

本研究旨在通过学习可转移的接触模型来实现空中操纵任务的无人机自主计算新型载荷的接触点，并在不进行手工特征制作或采用特定启发式算法的情况下对空中运输任务进行编码和增强，通过模拟实验得出结果，表明该方法对于空中运输任务可以生成更好的接触点。

自主空中操作的单次学习

One-shot Learning for Autonomous Aerial Manipulation

This paper presents a novel telepresence system for enhancing aerial
manipulation capabilities. It involves not only a haptic device, but also a
virtual reality that provides a 3D visual feedback to a remotely-located
teleoperator in real-time. We achieve this by utilizing onboard visual and
inertial sensors, an object tracking algorithm and a pre-generated object
database. As the virtual reality has to closely match the real remote scene, we
propose an extension of a marker tracking algorithm with visual-inertial
odometry. Both indoor and outdoor experiments show benefits of our proposed
system in achieving advanced aerial manipulation tasks, namely grasping,
placing, force exertion and peg-in-hole insertion.

本文提出了一种新型的遥操作系统，其结合了触觉设备和虚拟现实，为遥操作者提供 3D 视觉反馈，通过利用机载视觉和惯性传感器、对象跟踪算法和对象数据库实现。通过室内和室外实验，我们证明了提出的系统在实现高级空中操作任务方面的优势，包括抓取、放置、施力和插销孔插入。