The MORPHA style guide for icon-based programming

Bischoff, Rainer; Kazi, Arif; Seyfarth, M.

doi:10.1109/roman.2002.1045668

Cited by 39 publications

(16 citation statements)

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“…In particular, it has found good applications in robotics to improve HRI, such as teleoperations [5][6][7], industrial operations [8,9], etc., where AR can assist the operators in planning and simulating the task through interacting with the spatial environment prior to actual task execution. It has been reported that AR-based interfaces provide the means to maintain the situational awareness [7], as well as to facilitate different levels of HRI, such as the understanding of the robot perception of the world during debugging and development of the robot programs [10], the extension of the operator's perception of the real environment during robotic task planning and manipulation [11,12], as well as the integration of various interaction modalities in localization, navigation and planning of the mobile robots [13,14], etc. In most of the cases, AR-based human-robot interfaces permit the operators to visualize both the virtual information and real world environment simultaneously, where the virtual elements represent visual cues and enhancements for better understanding of the environment in robot task planning and execution [6,12,13].…”

Section: Introductionmentioning

confidence: 99%

Novel AR-based interface for human-robot interaction and visualization

2014

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Intuitive and efficient interfaces for humanrobot interaction (HRI) has been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This paper presents a novel augmented reality (AR) based interface to facilitate human-virtual robot interaction. A number of human-virtual robot interaction methods have been formulated and implemented with respect to the various types of operations needed in different robotic applications. A Euclidean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment. A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods, and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot. Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.

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Section: Introductionmentioning

confidence: 99%

Novel AR-based interface for human-robot interaction and visualization

2014

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“…human gestures) [8,43] have been employed to teach assembly line robots. However, these methods either required the user to demonstrate an optimal trajectory or interactively show the complete sequence of actions which the robot remembered for future use.…”

Section: Related Workmentioning

confidence: 99%

Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback

Jain

Sharma

Saxena

2016

Springer Tracts in Advanced Robotics

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We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than those arising from simple geometric constraints on robot's trajectory, such as distance of the robot from human etc. Our preferences are rather governed by the surrounding context of various objects and human interactions in the environment. Such preferences makes the problem challenging because the criterion of defining a good trajectory now varies with the task, with the environment and across the users. Furthermore, demonstrating optimal trajectories (e.g., learning from expert's demonstrations) is often challenging and non-intuitive on high degrees of freedom manipulators. In this work, we propose an approach that requires a non-expert user to only incrementally improve the trajectory currently proposed by the robot. We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings -household chores and grocery store checkout -and show that users are able to train the robot with just a few feedbacks (taking only a few minutes). Despite receiving sub-optimal feedback from non-expert users, our algorithm enjoys theoretical bounds on regret that match the asymptotic rates of optimal trajectory algorithms.

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“…Other well known languages are LabView, UML, MATLAB/Simulink and RCX. Using a touch screen as input device, icon-based programming languages such as in [24] can also lower the threshold to robot programming. There are also experimental systems using human programmer's gestures as a tool for pointing the intended robot locations [25].…”

Section: Related Workmentioning

confidence: 99%

From High-Level Task Descriptions to Executable Robot Code

Stenmark

Malec

Stolt

2015

Advances in Intelligent Systems and Computing

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Abstract. For robots to be productive co-workers in the manufacturing industry, it is necessary that their human colleagues can interact with them and instruct them in a simple manner. The goal of our research is to lower the threshold for humans to instruct manipulation tasks, especially sensor-controlled assembly. In our previous work we have presented tools for high-level task instruction, while in this paper we present how these symbolic descriptions of object manipulation are translated into executable code for our hybrid industrial robot controllers.

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The MORPHA style guide for icon-based programming

Cited by 39 publications

References 1 publication

Novel AR-based interface for human-robot interaction and visualization

Novel AR-based interface for human-robot interaction and visualization

Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback

From High-Level Task Descriptions to Executable Robot Code

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