Collaborative Autonomy Between High-Level Behaviors and Human Operators for Control of Complex Tasks with Different Humanoid Robots

2018 ◽  
pp. 429-494 ◽  
Author(s):  
David C. Conner ◽  
Stefan Kohlbrecher ◽  
Philipp Schillinger ◽  
Alberto Romay ◽  
Alexander Stumpf ◽  
...  
Keyword(s):  
Humanoid Robots ◽  
Complex Tasks ◽  
Human Operators ◽  
High Level ◽  
Collaborative Autonomy

Collaborative Autonomy between High-level Behaviors and Human Operators for Remote Manipulation Tasks using Different Humanoid Robots

2016 ◽  
Vol 34 (2) ◽  
pp. 333-358 ◽  
Author(s):  
Alberto Romay ◽  
Stefan Kohlbrecher ◽  
Alexander Stumpf ◽  
Oskar von Stryk ◽  
Spyros Maniatopoulos ◽  
...  
Keyword(s):  
Humanoid Robots ◽  
Remote Manipulation ◽  
Human Operators ◽  
High Level ◽  
Collaborative Autonomy

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals Transportable open-source application program interface and user interface for generic humanoids: TOUGH

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092160
Author(s):  
Vinayak Jagtap ◽  
Shlok Agarwal ◽  
Ameya Wagh ◽  
Michael Gennert
Keyword(s):  
User Interface ◽  
Open Source ◽  
Humanoid Robots ◽  
Application Program Interface ◽  
Sensors And Actuators ◽  
Seamless Integration ◽  
Application Program ◽  
Diverse Field ◽  
High Level ◽  
Program Interface

Humanoid robotics is a complex and highly diverse field. Humanoid robots may have dozens of sensors and actuators that together realize complicated behaviors. Adding to the complexity is that each type of humanoid has unique application program interfaces, thus software written for one humanoid does not easily transport to others. This article introduces the transportable open-source application program interface and user interface for generic humanoids, a set of application program interfaces that simplifies the programming and operation of diverse humanoid robots. These application program interfaces allow for quick implementation of complex tasks and high-level controllers. Transportable open-source application program interface and user interface for generic humanoids has been developed for, and tested on, Boston Dynamics’ Atlas V5 and NASA’s Valkyrie R5 robots. It has proved successful for experiments on both robots in simulation and hardware, demonstrating the seamless integration of manipulation, perception, and task planning. To encourage the rapid adoption of transportable open-source application program interface and user interface for generic humanoids for education and research, the software is available as Docker images, which enable quick setup of multiuser simulation environments.

scholarly journals A Robotic Cognitive Architecture for Slope and Dam Inspections

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4579 ◽  
Author(s):  
Milena F. Pinto ◽  
Leonardo M. Honorio ◽  
Aurélio Melo ◽  
Andre L. M. Marcato
Keyword(s):  
Decision Making ◽  
Cognitive Architecture ◽  
Electrical Power ◽  
Data Gathering ◽  
Path Following ◽  
Sensors And Actuators ◽  
Construction Enterprises ◽  
Performance Quality ◽  
Human Operators ◽  
High Level

Big construction enterprises, such as electrical power generation dams and mining slopes, demand continuous visual inspections. The sizes of these structures and the necessary level of detail in each mission requires a conflicting set of multi-objective goals, such as performance, quality, and safety. It is challenging for human operators, or simple autonomous path-following drones, to process all this information, and thus, it is common that a mission must be repeated several times until it succeeds. This paper deals with this problem by developing a new cognitive architecture based on a collaborative environment between the unmanned aerial vehicles (UAVs) and other agents focusing on optimizing the data gathering, information processing, and decision-making. The proposed architecture breaks the problem into independent units ranging from sensors and actuators up to high-level intelligence processes. It organizes the structures into data and information; each agent may request an individual behavior from the system. To deal with conflicting behaviors, a supervisory agent analyzes all requests and defines the final planning. This architecture enables real-time decision-making with intelligent social behavior among the agents. Thus, it is possible to process and make decisions about the best way to accomplish the mission. To present the methodology, slope inspection scenarios are shown.

Whole-Body Balancing Walk Controller for Position Controlled Humanoid Robots

2016 ◽  
Vol 13 (01) ◽  
pp. 1650011 ◽  
Author(s):  
Seung-Joon Yi ◽  
Byoung-Tak Zhang ◽  
Dennis Hong ◽  
Daniel D. Lee
Keyword(s):  
Hierarchical Control ◽  
Humanoid Robots ◽  
Whole Body ◽  
Appropriate Behavior ◽  
Low Level ◽  
Push Recovery ◽  
Hierarchical Control Architecture ◽  
High Level ◽  
Experimental Trials ◽  
Decision Boundaries

Bipedal humanoid robots are intrinsically unstable against unforeseen perturbations. Conventional zero moment point (ZMP)-based locomotion algorithms can reject perturbations by incorporating sensory feedback, but they are less effective than the dynamic full body behaviors humans exhibit when pushed. Recently, a number of biomechanically motivated push recovery behaviors have been proposed that can handle larger perturbations. However, these methods are based upon simplified and transparent dynamics of the robot, which makes it suboptimal to implement on common humanoid robots with local position-based controllers. To address this issue, we propose a hierarchical control architecture. Three low-level push recovery controllers are implemented for position controlled humanoid robots that replicate human recovery behaviors. These low-level controllers are integrated with a ZMP-based walk controller that is capable of generating reactive step motions. The high-level controller constructs empirical decision boundaries to choose the appropriate behavior based upon trajectory information gathered during experimental trials. Our approach is evaluated in physically realistic simulations and on a commercially available small humanoid robot.

scholarly journals Behavior recognition for humanoid robots using long short-term memory

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141666336 ◽  
Author(s):  
Dickson Neoh Tze How ◽  
Chu Kiong Loo ◽  
Khairul Salleh Mohamed Sahari
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Building Blocks ◽  
Humanoid Robots ◽  
Learning From Demonstration ◽  
Short Term ◽  
Term Memory ◽  
Complex Tasks ◽  
Long Short Term Memory

Learning from demonstration plays an important role in enabling robot to acquire new behaviors from human teachers. Within learning from demonstration, robots learn new tasks by recognizing a set of preprogrammed behaviors or skills as building blocks for new, potentially more complex tasks. One important aspect in this approach is the recognition of the set of behaviors that comprises the entire task. The ability to recognize a complex task as a sequence of simple behaviors enables the robot to generalize better on more complex tasks. In this article, we propose that primitive behaviors can be taught to a robot via learning from demonstration. In our experiment, we teach the robot new behaviors by demonstrating the behaviors to the robot several times. Following that, a long short-term memory recurrent neural network is trained to recognize the behaviors. In this study, we managed to teach at least six behaviors on a NAO humanoid robot and trained a long short-term memory recurrent neural network to recognize the behaviors using the supervised learning scheme. Our result shows that long short-term memory can recognize all the taught behaviors effectively, and it is able to generalize to recognize similar types of behaviors that have not been demonstrated on the robot before. We also show that the long short-term memory is advantageous compared to other neural network frameworks in recognizing the behaviors in the presence of noise in the behaviors.

scholarly journals Whole-Body Operational Space Control for Locomotion and Manipulation

2015 ◽  
Vol 137 (06) ◽  
pp. S2-S6
Author(s):  
Luis Sentis
Keyword(s):  
High Speed ◽  
Degrees Of Freedom ◽  
Humanoid Robots ◽  
Rigid Body Dynamics ◽  
Joint Torque ◽  
Whole Body ◽  
Operational Space ◽  
Body Dynamics ◽  
Service Oriented ◽  
High Level

This article discusses the various researches being undertaken to study and develop Whole-Body Operational Space Control (WBOSC). The WBOSC emerges as a capable framework for real-time unified control of motion and force of humanoid robots. It could theoretically outperform high-speed industrial manipulators while providing the grounds for new types of service-oriented applications that require contact, by exploiting the rigid body dynamics of systems. By relying on joint torque sensors, WBOSC opens up the potential to interact with the physical environment using any part of the robot’s body while regulating the effective mechanical impedances to safe values. With ControlIt!, the developers provide a strict and easy way to use the WBOSC API consisting of compound tasks which define the operational space, and constraint sets that define the contacts with the environment as well as dependent degrees of freedom. ControlIt! is easy to connect to high level planners.

Separation of low-level and high-level factors in complex tasks: Visual search.

1995 ◽  
Vol 102 (2) ◽  
pp. 356-378 ◽  
Author(s):  
Wilson S. Geisler ◽  
Kee-Lee Chou
Keyword(s):  
Visual Search ◽  
Complex Tasks ◽  
Low Level ◽  
High Level
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