Grounding of actions based on verbalized physical effects and manipulation primitives

Spangenberg, Michael; Henrich, Dominik

doi:10.1109/iros.2015.7353470

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Furthermore, we plan to extend our framework to complex and combined VPEs, and the support of additional user interfaces. For detailed description of the base concept for the grounding of actions and the execution on the KUKA LWR, please see our work [9].…”

Section: Discussionmentioning

confidence: 99%

“…The type of representation denotes either a specific symbol like a verb or a general symbol like a task description. A detailed overview of the approaches is shown in our previous work [9]. In this work, we describe the characteristics of the approaches in general.…”

Section: Related Workmentioning

confidence: 99%

“…Our DSL utilizes the concept of verbalized physical effects. This concept is used to ground symbols in form of verbs to a robot system and transform symbolic into subsymbolic information and is described in detail in our previous work [9].…”

Section: Related Workmentioning

confidence: 99%

“…The transformation between the defined (elementary) physical effects and the corresponding manipulation primitive nets is described in our previous work [9]. In this work, we describe a flexible representation for physical effects based on an extensible markup language (XML) container, representing a grounded skeleton of a specific verbalized physical effect.…”

Section: Grounding Of Actionsmentioning

confidence: 99%

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Towards a Domain Specific Language for Sensor-Based Actions

Spangenberg

Henrich

2016

AMM

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Actual robot systems are typically used within an industrial environment, where they perform tasks in the areas of manufacturing, assembly, or inspection. Many of these tasks require the definition and execution of sensor-based motions. Such motions are typically defined using a subsymbolic representation like manipulation primitive nets [1] or iTaSC [2]. These representations are only suitable for experts in the domain of robotics because they require the definition of low-level parameters like setpoints, control strategies, or task frames. Since the application domains of robot systems shall be extended to SMEs or private households, existing representations lack in an intuitive interface for users without expert knowledge in the area of robotics. In this work, we describe an intuitive interface for the definition of sensor-based actions. Our approach is based on manipulation primitive nets and consists of a transformation between the subsymbolic manipulation primitive nets and a symbolic user interface, described by a domain specific language.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Grounding Of Actionsmentioning

confidence: 99%

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Towards a Domain Specific Language for Sensor-Based Actions

Spangenberg

Henrich

2016

AMM

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“…There has been some work in the robotics community to translate natural language to robotic operations (Kress-Gazit et al, 2007;Jia et al, 2014;Spangenberg and Henrich, 2015), but not for the purpose of learning new actions. To support action learning, previously we have developed a system where the robot can acquire the meaning of a new verb (e.g., stack) by following human's step-by-step language instructions (She et al, 2014a;She et al, 2014b).…”

Section: Related Workmentioning

confidence: 99%

Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction

She

Chai²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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As a new generation of cognitive robots start to enter our lives, it is important to enable robots to follow human commands and to learn new actions from human language instructions. To address this issue, this paper presents an approach that explicitly represents verb semantics through hypothesis spaces of fluents and automatically acquires these hypothesis spaces by interacting with humans. The learned hypothesis spaces can be used to automatically plan for lower-level primitive actions towards physical world interaction. Our empirical results have shown that the representation of a hypothesis space of fluents, combined with the learned hypothesis selection algorithm, outperforms a previous baseline. In addition, our approach applies incremental learning, which can contribute to life-long learning from humans in the future.

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Symbolic Robot Commanding Utilizing Physical Properties - System Overview

Spangenberg

Henrich

2016

KI 2016: Advances in Artificial Intelligence

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Grounding of actions based on verbalized physical effects and manipulation primitives

Cited by 4 publications

References 25 publications

Towards a Domain Specific Language for Sensor-Based Actions

Towards a Domain Specific Language for Sensor-Based Actions

Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction

Symbolic Robot Commanding Utilizing Physical Properties - System Overview

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