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This article presents a novel telepresence system for advancing aerialmanipulation in dynamic and unstructured environments. The proposed system notonly features a haptic device, but also a virtual reality (VR) interface thatprovides real-time 3D displays of the robot's workspace as well as a hapticguidance to its remotely located operator. To realize this, multiple sensorsnamely a LiDAR, cameras and IMUs are utilized. For processing of the acquiredsensory data, pose estimation pipelines are devised for industrial objects ofboth known and unknown geometries. We further propose an active learningpipeline in order to increase the sample efficiency of a pipeline componentthat relies on Deep Neural Networks (DNNs) based object detection. All thesealgorithms jointly address various challenges encountered during the executionof perception tasks in industrial scenarios. In the experiments, exhaustiveablation studies are provided to validate the proposed pipelines.Methodologically, these results commonly suggest how an awareness of thealgorithms' own failures and uncertainty (`introspection') can be used tacklethe encountered problems. Moreover, outdoor experiments are conducted toevaluate the effectiveness of the overall system in enhancing aerialmanipulation capabilities. In particular, with flight campaigns over days andnights, from spring to winter, and with different users and locations, wedemonstrate over 70 robust executions of pick-and-place, force application andpeg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As aresult, we show the viability of the proposed system in future industrialapplications.

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