This paper provides a system overview about ANYmal, a quadrupedal robot developed for operation in harsh environments. The 30 kg, 0.5 m tall robotic dog was built in a modular way for simple maintenance and user-friendly handling, while focusing on high mobility and dynamic motion capability. The system is tightly sealed to reach IP67 standard and protected to survive falls. Rotating lidar sensors in the front and back are used for localization and terrain mapping and compact force sensors in the feet provide accurate measurements about the contact situations. The variable payload, such as a modular pan-tilt head with a variety of inspection sensors, can be exchanged depending on the application. Thanks to novel, compliant joint modules with integrated electronics, ANYmal is precisely torque controllable and very robust against impulsive loads during running or jumping. In a series experiments we demonstrate that ANYmal can execute various climbing maneuvers, walking gaits, as well as a dynamic trot and jump. As special feature, the joints can be fully rotated to switch between X-and O-type kinematic configurations. Detailed measurements unveil a low energy consumption of 280 W during locomotion, which results in an autonomy of more than 2 h.

We show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an offthe-shelf deep reinforcement learning algorithm, we trained a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of threedimensional (re-)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-toreal transfer, we deploy trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for two-dimensional micro-gravity experiments. The experimental results demonstrate that repetitive, controlled jumping and landing with natural agility is possible.Index Terms-Legged Robots; Deep Learning in Robotics and Automation; Space Robotics and Automation * We choose to use the gravity of Ceres, −0.27m/s 2 ≈ 0.03g [22]. Ceres is the smallest dwarf planet of the solar system. The low gravity environment makes it an interesting target for jumping locomotion.† https://youtu.be/KQhlZa42fe4

This paper introduces SpaceBok, a quadrupedal robot created to investigate dynamic legged locomotion for the exploration of low-gravity celestial bodies. With a hip height of 500 mm and a mass of 20 kg, its dimensions are comparable to a medium-sized dog. The robot's leg configuration is based on an optimized parallel motion mechanism that allows the integration of parallel elastic elements to store and release energy for powerful jumping maneuvers. High-torque brushless motors in combination with customized single-stage planetary gear transmissions enable force control at the foot contact points based on motor currents. We present successful walking, trotting, and pronking experiments. Thereby, Spacebok achieved maximal jump heights in single jump experiments of up to 1.05 m (more than twice the hip height) and a walking velocity of 1 m /s. Moreover, simulation results for low gravity on the moon suggest that our robot can move with up to 1.1 m /s at an approximate cost of transport of 1 in moon gravity when using the pronking gait.

The regular inspection of sewer systems is essential to assess the level of degradation and to plan maintenance work. Currently, human inspectors must walk through sewers and use their sense of touch to inspect the roughness of the floor and check for cracks. The sense of touch is used since the floor is often covered by (waste) water and biofilm, which renders visual inspection very challenging. In this paper, we demonstrate a robotic inspection system which evaluates concrete deterioration using tactile interaction. We deployed the quadruped robot ANYmal in the sewers of Zurich and commanded it using shared autonomy for several such missions. The inspection itself is realized via a well-defined scratching motion using one of the limbs on the sewer floor. Inertial and force/torque sensors embedded within specially designed feet captured the resulting vibrations. A pretrained support vector machine (SVM) is evaluated to assess the state of the concrete. The results of the classification are then displayed in a three-dimensional map recorded by the robot for easy visualization and assessment. To train the SVM we recorded 625 samples with ground truth labels provided by professional sewer inspectors. We make this data set publicly available. We achieved deterioration level estimates within three classes of more than 92% accuracy. During the four deployment missions, we covered a total distance of 300 m and acquired 130 inspection samples.

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