Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. This often precludes learning on the physical robot, as it may take many trials to learn the constraints, and the necessity to violate them during the trial-and-error learning may be unsafe. We have serendipitously discovered that constraint representations for shared control - in particular Shared Control Templates (SCTs) - are ideally suited for safely guiding RL. Representing constraints explicitly, rather than implicitly in the reward function, also simplifies the design of the reward function. The main advantage of the approach is safer, faster learning without constraint violations (even with sparse reward functions). We demonstrate this in a pouring task in simulation and on a real robot, where learning the task requires only 65 episodes in 16 minutes.

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Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into account. The optimized trajectories yield flight paths within the apex angle of a Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to perceive obstacles in the flight direction. Furthermore, the optimized trajectories take the flight dynamics into account and contain the velocities and accelerations along the path. We evaluate our approach with a DJI Matrice 600 MAV and in simulation employing hardware-in-the-loop.

Complex indoor and outdoor missions for autonomous micro aerial vehicles (MAV) require fast generation of collision-free paths in 3D space. Often not all obstacles in an environment are known prior to the mission execution. Consequently, the ability for replanning during a flight is key for success. Our approach locally optimizes trajectories of grid-based path planning. It preserves obstacle-freeness of the path and ensures smoothness with continuous curvature transition segments. Fast optimization and frequent reoptimization is made possible by means of local multiresolution time discretization. With our extensions, high dimensional flight trajectories incorporating velocities and accelerations can be planned with a time discretization of 100 Hz within the prediction horizon of the underlying controller.

Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitive-based approach. Our approach learns object models from single scans and employs active perception to cope with severe occlusions. Grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

Domestic service tasks require three main skills from autonomous robots: robust navigation in indoor environments, flexible object manipulation, and intuitive communication with the users. In this report, we present the communication skills of our anthropomorphic service and communication robots Dynamaid and Robotinho. Both robots are equipped with an intuitive multimodal communication system, including speech synthesis and recognition, gestures and mimic. We evaluate our systems in the @Home league of the RoboCup competitions and in a museum tour guide scenario.

In the last decades, tremendous progress has been made in the field of autonomous indoor navigation for mobile robots. However, these approaches assume the structural part of the environment to be completely static. In practice, movable parts of scenes, e.g. doors, frequently violate this assumption which leads to poor performance. Also, mobile manipulation capabilities can only be utilized, if the robot knows about the movability of objects. In this paper, we address an important part of these problems by the explicit representation of doors as door leaves and joints. We propose to augment standard approaches to navigation like 2D occupancy grid mapping and Monte-Carlo-Localization. Our algorithm detects doors during mapping and represents their movability adequately in the map. During localization, the state of doors is estimated from measurements while it is simultaneously used to improve localization robustness and accuracy. In experimental results we demonstrate superior performance of our method compared to a state-of-the-art approach to localization.