Christian Lenz

Max Schwarz, Christian Lenz, Andre Rochow et al.

Robotic avatars promise immersive teleoperation with human-like manipulation and communication capabilities. We present such an avatar system, based on the key components of immersive 3D visualization and transparent force-feedback telemanipulation. Our avatar robot features an anthropomorphic bimanual arm configuration with dexterous hands. The remote human operator drives the arms and fingers through an exoskeleton-based operator station, which provides force feedback both at the wrist and for each finger. The robot torso is mounted on a holonomic base, providing locomotion capability in typical indoor scenarios, controlled using a 3D rudder device. Finally, the robot features a 6D movable head with stereo cameras, which stream images to a VR HMD worn by the operator. Movement latency is hidden using spherical rendering. The head also carries a telepresence screen displaying a synthesized image of the operator with facial animation, which enables direct interaction with remote persons. We evaluate our system successfully both in a user study with untrained operators as well as a longer and more complex integrated mission. We discuss lessons learned from the trials and possible improvements.

Christian Lenz, Sven Behnke

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a 1:1 mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed directly to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. Only off-the-shelf components were used to build the system. It is evaluated in lab experiments and by untrained operators in a small user study.

Jan Quenzel, Malte Splietker, Dmytro Pavlichenko et al.

Every day, burning buildings threaten the lives of occupants and first responders trying to save them. Quick action is of essence, but some areas might not be accessible or too dangerous to enter. Robotic systems have become a promising addition to firefighting, but at this stage, they are mostly manually controlled, which is error-prone and requires specially trained personal. We present two systems for autonomous firefighting from air and ground we developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The systems use LiDAR for reliable localization within narrow, potentially GNSS-restricted environments while maneuvering close to obstacles. Measurements from LiDAR and thermal cameras are fused to track fires, while relative navigation ensures successful extinguishing. We analyze and discuss our successful participation during the MBZIRC 2020, present further experiments, and provide insights into our lessons learned from the competition.

Christian Lenz, Jan Quenzel, Arul Selvam Periyasamy et al.

Autonomous robotic systems for various applications including transport, mobile manipulation, and disaster response are becoming more and more complex. Evaluating and analyzing such systems is challenging. Robotic competitions are designed to benchmark complete robotic systems on complex state-of-the-art tasks. Participants compete in defined scenarios under equal conditions. We present our UGV solution developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Our hard- and software components to address the challenge tasks of wall building and fire fighting are integrated into a fully autonomous system. The robot consists of a wheeled omnidirectional base, a 6 DoF manipulator arm equipped with a magnetic gripper, a highly efficient storage system to transport box-shaped objects, and a water spraying system to fight fires. The robot perceives its environment using 3D LiDAR as well as RGB and thermal camera-based perception modules, is capable of picking box-shaped objects and constructing a pre-defined wall structure, as well as detecting and localizing heat sources in order to extinguish potential fires. A high-level planner solves the challenge tasks using the robot skills. We analyze and discuss our successful participation during the MBZIRC 2020 finals, present further experiments, and provide insights to our lessons learned.

Christian Lenz, Max Schwarz, Andre Rochow et al.

Constructing large structures with robots is a challenging task with many potential applications that requires mobile manipulation capabilities. We present two systems for autonomous wall building that we developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Both systems autonomously perceive their environment, find bricks, and build a predefined wall structure. While the UGV uses a 3D LiDAR-based perception system which measures brick poses with high precision, the UAV employs a real-time camera-based system for visual servoing. We report results and insights from our successful participation at the MBZIRC 2020 Finals, additional lab experiments, and discuss the lessons learned from the competition.

Dmytro Pavlichenko, Diego Rodriguez, Christian Lenz et al.

In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. This imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. Thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. This process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. The latter is used to compensate for the unpredictable object movement during the first grasp. The approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor.

Tobias Klamt, Diego Rodriguez, Lorenzo Baccelliere et al.

Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promising to perform mission tasks from a safe distance. In the CENTAURO project, we developed a disaster-response system which consists of the highly flexible Centauro robot and suitable control interfaces including an immersive tele-presence suit and support-operator controls on different levels of autonomy. In this article, we give an overview of the final CENTAURO system. In particular, we explain several high-level design decisions and how those were derived from requirements and extensive experience of Kerntechnische Hilfsdienst GmbH, Karlsruhe, Germany (KHG). We focus on components which were recently integrated and report about a systematic evaluation which demonstrated system capabilities and revealed valuable insights.

Patrick Stotko, Stefan Krumpen, Max Schwarz et al.

Applications like disaster management and industrial inspection often require experts to enter contaminated places. To circumvent the need for physical presence, it is desirable to generate a fully immersive individual live teleoperation experience. However, standard video-based approaches suffer from a limited degree of immersion and situation awareness due to the restriction to the camera view, which impacts the navigation. In this paper, we present a novel VR-based practical system for immersive robot teleoperation and scene exploration. While being operated through the scene, a robot captures RGB-D data that is streamed to a SLAM-based live multi-client telepresence system. Here, a global 3D model of the already captured scene parts is reconstructed and streamed to the individual remote user clients where the rendering for e.g. head-mounted display devices (HMDs) is performed. We introduce a novel lightweight robot client component which transmits robot-specific data and enables a quick integration into existing robotic systems. This way, in contrast to first-person exploration systems, the operators can explore and navigate in the remote site completely independent of the current position and view of the capturing robot, complementing traditional input devices for teleoperation. We provide a proof-of-concept implementation and demonstrate the capabilities as well as the performance of our system regarding interactive object measurements and bandwidth-efficient data streaming and visualization. Furthermore, we show its benefits over purely video-based teleoperation in a user study revealing a higher degree of situation awareness and a more precise navigation in challenging environments.

Tobias Klamt, Max Schwarz, Christian Lenz et al.

Solving mobile manipulation tasks in inaccessible and dangerous environments is an important application of robots to support humans. Example domains are construction and maintenance of manned and unmanned stations on the moon and other planets. Suitable platforms require flexible and robust hardware, a locomotion approach that allows for navigating a wide variety of terrains, dexterous manipulation capabilities, and respective user interfaces. We present the CENTAURO system which has been designed for these requirements and consists of the Centauro robot and a set of advanced operator interfaces with complementary strength enabling the system to solve a wide range of realistic mobile manipulation tasks. The robot possesses a centaur-like body plan and is driven by torque-controlled compliant actuators. Four articulated legs ending in steerable wheels allow for omnidirectional driving as well as for making steps. An anthropomorphic upper body with two arms ending in five-finger hands enables human-like manipulation. The robot perceives its environment through a suite of multimodal sensors. The resulting platform complexity goes beyond the complexity of most known systems which puts the focus on a suitable operator interface. An operator can control the robot through a telepresence suit, which allows for flexibly solving a large variety of mobile manipulation tasks. Locomotion and manipulation functionalities on different levels of autonomy support the operation. The proposed user interfaces enable solving a wide variety of tasks without previous task-specific training. The integrated system is evaluated in numerous teleoperated experiments that are described along with lessons learned.

Dmytro Pavlichenko, Diego Rodriguez, Max Schwarz et al.

Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios. However, this is a challenging undertaking, particularly in terms of perception and planning. Unstructured scenarios are full of objects with different shapes and appearances that have to be grasped in a very specific manner so they can be functionally used. In this paper we present an integrated approach to perform dual-arm pick tasks autonomously. Our method consists of semantic segmentation, object pose estimation, deformable model registration, grasp planning and arm trajectory optimization. The entire pipeline can be executed on-board and is suitable for on-line grasping scenarios. For this, our approach makes use of accumulated knowledge expressed as convolutional neural network models and low-dimensional latent shape spaces. For manipulating objects, we propose a stochastic trajectory optimization that includes a kinematic chain closure constraint. Evaluation in simulation and on the real robot corroborates the feasibility and applicability of the proposed methods on a task of picking up unknown watering cans and drills using both arms.

Max Schwarz, David Droeschel, Christian Lenz et al.

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state-of-the-art in autonomous operation of ground-based and flying robots. In this article, we describe our winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is capable of autonomously solving a valve manipulation task using a wrench tool detected, grasped, and finally employed to turn a valve stem. Mario's omnidirectional base allows both fast locomotion and precise close approach to the manipulation panel. We describe an efficient detector for medium-sized objects in 3D laser scans and apply it to detect the manipulation panel. An object detection architecture based on deep neural networks is used to find and select the correct tool from grayscale images. Parametrized motion primitives are adapted online to percepts of the tool and valve stem in order to turn the stem. We report in detail on our winning performance at the challenge and discuss lessons learned.

Max Schwarz, Christian Lenz, Germán Martín García et al.

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.

Max Schwarz, Tobias Rodehutskors, David Droeschel et al.

Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g. for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four legs ending in steerable wheels, which allows it both to drive omnidirectionally and to step over obstacles or climb. Furthermore, we present advanced communication and teleoperation approaches, which include immersive 3D visualization, and 6D tracking of operator head and arm motions. The proposed system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Cup Qualification and lab experiments. We also discuss the lessons learned from the competitions.

Tobias Klamt, Diego Rodriguez, Max Schwarz et al.

Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.