Search

This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to the MIRROR (i.e., the Multi-arm Installation Robot for Readying ORUs and Reflectors) use-case scenario as proposed by the European Space Agency. A holistic computational design and validation pipeline is proposed, with the aim of comparing different limb designs, as well as ensuring that valid limb candidates enable MARM to perform the complex loco-manipulation tasks required. Motivated by the task complexity in terms of kinematic reachability, (self)-collision avoidance, contact wrench limits, and motor torque limits affecting Earth experiments, this work leverages on multiple state-of-art planning and control approaches to aid the robot design and validation. These include sampling-based planning on manifolds, non-linear trajectory optimization, and quadratic programs for inverse dynamics computations with constraints. Finally, we present the attained MARM design and conduct preliminary tests for hardware validation through a set of lab experiments., Comment: 7 pages (last page references), 15 figures, accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2023

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., Comment: Accepted for IEEE Robotics and Automation Magazine (RAM), to appear December 2019

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., Comment: Published in: Journal of Field Robotics (JFR), Wiley, 2019

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.