Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots

Walker, Nick; Peng, Yu-Tang; Çakmak, Maya

doi:10.1007/978-3-030-35699-6_26

“…These dynamic characteristics include: system calls, file read and write operations, network communication behaviors, and process behaviors. For example, Liang P used a variant of the rule learning algorithm to learn the rule information in system calls to detect new types of malware behaviors 12 . In addition, considering that in dynamic analysis methods, data collection is frequently performed, therefore, appropriate behavioral capture tools are needed, Willems et al developed CWSandbox Sandbox, which can automatically and quickly analyze Win32.…”

Section: Analytical Methods For Malware Detectionmentioning

confidence: 99%

Malware behavior detection method based on reinforcement learning

Cui

¹

,

Leng

²

,

Wang

³

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

0

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Malware in the network environment is a serious threat to the security of industrial control systems. With the gradual increase of malware variants, it brings great challenges to the detection and security protection of industrial control system malware. The existing detection methods have limitations such as low intelligence in adaptive detection and recognition. In response to this problem, this paper designs a detection application method framework by combining the use of reinforcement learning, an advanced machine learning algorithm, around the malware objects that threaten the network security of industrial control systems. In the implementation process, according to the actual needs of malware behavior detection, fully combined with intelligent features such as sequential decision-making and dynamic feedback learning of reinforcement learning, the key application modules such as feature extraction network, policy network and classification network are discussed and designed in detail. The application experiments based on the actual malware test data set verify the effectiveness of the method in this paper, which can provide an intelligent decision-making aid for general malware behavior detection.

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“…When the desired requirements are achieved, the authoring process is finished and the robot is ready to be deployed to interact autonomously. Authoring differs from classic programming in its focus on end users with limited background in computer sciences and seeks to address questions of how can these users design, or author, behaviors using modalities such as tangible interactions ( Sefidgar et al, 2017 ; Huang and Cakmak, 2017 ), natural language ( Walker et al, 2019 ), augmented- or mixed-reality ( Cao et al, 2019a ; Peng et al, 2018 ; Akan et al, 2011 ; Gao and Huang, 2019 ), visual programming environments ( Glas et al, 2016 ; Paxton et al, 2017 ), or a mixture of modalities ( Huang and Cakmak, 2017 ; Porfirio et al, 2019 ). Steinmetz et al (2018) describe task-level programming as parameterizing and sequencing predefined skills composed of primitives to solve a task at hand.…”

Section: Related Workmentioning

confidence: 99%

Task-Level Authoring for Remote Robot Teleoperation

Senft

¹

,

Hagenow

²

,

Welsh

³

et al. 2021

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Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach for enabling end users to effectively perform remote robot programming.

show abstract

“…When the desired requirements are achieved, the authoring process is finished and the robot is ready to be deployed to interact autonomously. Authoring differs from classic programming in its focus on end users with limited background in computer sciences and seeks to address questions of how can these users design, or author, behaviors using modalities such as tangible interactions (Sefidgar et al, 2017;Huang and Cakmak, 2017), natural language (Walker et al, 2019b), augmented-or mixed-reality (Cao et al, 2019a;Peng et al, 2018;Akan et al, 2011;Gao and Huang, 2019), visual programming environments (Glas et al, 2016;Paxton et al, 2017), or a mixture of modalities (Huang and Cakmak, 2017;Porfirio et al, 2019). Steinmetz et al (2018) describe task-level programming as parameterizing and sequencing predefined skills composed of primitives to solve a task at hand.…”

Section: Authoringmentioning

confidence: 99%

Task-Level Authoring for Remote Robot Teleoperation

Senft,

Hagenow,

Welsh

et al. 2021

Preprint

View full text Add to dashboard Cite

Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach for enabling end users to effectively perform remote robot programming.

show abstract

Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots

Cited by 14 publications

References 20 publications

Malware behavior detection method based on reinforcement learning

Malware behavior detection method based on reinforcement learning

Task-Level Authoring for Remote Robot Teleoperation

Task-Level Authoring for Remote Robot Teleoperation

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