Horizontally scanning 2D laser rangefinders are a popular approach for indoor robot localization because of the high accuracy of the sensors and the compactness of the required 2D maps. As the scanners in this configuration only provide information about one slice of the environment, the measurements typically do not capture the full extent of a large variety of obstacles, including chairs or tables. Accordingly, obstacle avoidance based on laser scanners mounted in such a fashion is likely to fail. In this paper, we propose a learning-based approach to predict collisions in 2D occupancy maps. Our approach is based on a convolutional neural network which is trained on a 2D occupancy map and collision events recorded with a bumper while the robot is navigating in its environment. As the network operates on local structures only, it can generalize to new environments. In addition, the robot can collect and integrate new collision examples after an initial training phase. Extensive experiments carried out in simulation and a realistic real-world environment confirm that our approach allows robots to learn from collision events to avoid collisions in the future.

Autonomous systems, such as delivery robots, are increasingly employed in indoor spaces to carry out activities alongside humans. This development poses the question of how robots can carry out their tasks while, at the same time, behaving in a socially compliant manner. Further, humans need to be able to communicate their preferences in a simple and intuitive way, and robots should adapt their behavior accordingly. This paper investigates force control as a natural means to interact with a mobile robot by pushing it along the desired trajectory. We employ inverse reinforcement learning (IRL) to learn from human interaction and adapt the robot behavior to its users' preferences, thereby eliminating the need to program the desired behavior manually. We evaluate our approach in a real-world experiment where test subjects interact with an autonomously navigating robot in close proximity. The results suggest that force control presents an intuitive means to interact with a mobile robot and show that our robot can quickly adapt to the test subjects' personal preferences.

Most of the conventional approaches to mobile robot navigation avoid any kind of contact with the environment or with humans. As nowadays distance sensors typically have a limited - and often only two-dimensional - field of view, collisions with the environment or contacts with humans cannot be fully avoided in practical mobile robot applications. On the other hand, direct physical contact can be used for intuitive communication between a robot and humans. In this paper, we present a whole-body sensory concept based on a 6-DoF force-torque sensor to perceive physical interaction between the robot and humans. To distinguish between external contact and disturbance forces that result from the motion of the mobile platform or oscillations, we present a novel model-free filtering approach based on a neural network. In extensive experiments carried out with our robot Canny we demonstrate the effectiveness and advantages of the neural network approach, which clearly outperforms a classical model-based one.