Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot

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.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 201

Hierarchical Collision Avoidance for Adaptive-Speed Multirotor Teleoperation

This paper improves safe motion primitives-based teleoperation of a multirotor by developing a hierarchical collision avoidance method that modulates maximum speed based on environment complexity and perceptual constraints. Safe speed modulation is challenging in environments that exhibit varying clutter. Existing methods fix maximum speed and map resolution, which prevents vehicles from accessing tight spaces and places the cognitive load for changing speed on the operator. We address these gaps by proposing a high-rate (10 Hz) teleoperation approach that modulates the maximum vehicle speed through hierarchical collision checking. The hierarchical collision checker simultaneously adapts the local map's voxel size and maximum vehicle speed to ensure motion planning safety. The proposed methodology is evaluated in simulation and real-world experiments and compared to a non-adaptive motion primitives-based teleoperation approach. The results demonstrate the advantages of the proposed teleoperation approach both in time taken and the ability to complete the task without requiring the user to specify a maximum vehicle speed.Comment: 8 pages, 8 figures, to be published in the 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR

Adaptive Mobile Manipulation for Articulated Objects In the Open World

Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/Comment: Website at https://open-world-mobilemanip.github.io

A review on manipulation skill acquisition through teleoperation-based learning from demonstration

Manipulation skill learning and generalization have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realize the manipulation skill learning and generalization. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and the teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalization to deal with complex manipulation tasks. To this end, the key technologies, e.g. manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation

Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten. Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen

A climbing autonomous robot for inspection application in 3D complex environment

Often inspection and maintenance work involve a large number of highly dangerous manual operations, especially within industrial fields such as shipbuilding and construction. This paper deals with the autonomous climbing robot which uses the “caterpillar” concept to climb in complex 3D metallic-based structures. During its motion the robot generates in real-time the path and grasp planning in order to ensure stable self-support to avoid the environment obstacles, and to optimise the robot consumption during the inspection. The control and monitoring of the robot is achieved through an advanced Graphical User Interface to allow an effective and user friendly operation of the robot. The experiments confirm its advantages in executing the inspection operations.This work has been partially funded by the Spanish government agency CICYT under project TAP95-0088. The authors would like to acknowledge the technical support of A. Jardón, E. Jiménez, C. Palazuelos, J.A. Campo and F. Manera and also the company of APTECA for its help in the mechanical development.Publicad