Collaborative Programming of Conditional Robot Tasks

Willibald, Christoph; Eiband, Thomas; Lee, Dongheui

doi:10.1109/iros45743.2020.9341212

Cited by 5 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many industrial applications, skill parameters are still manually defined [31]. However, recent works consider automatic parameterization techniques, where the skill sequence and skill parameters are defined either by an autonomous planner or extracted from human demonstrations [20], [21]. Also the complexity of parameters itself is changing from simple, physical quantities such as positions towards more abstract ones, such as object IDs or even interfaces to world models which are passed as parameters [5].…”

Section: Trends and Outlooksmentioning

confidence: 99%

Capability-based Frameworks for Industrial Robot Skills: a Survey

Pantano,

Eiband,

Lee

2022

Preprint

Self Cite

View full text Add to dashboard Cite

The research community is puzzled with words like skill, action, atomic unit and others when trying to describe robot capabilities. However, for giving the possibility to integrate such in the industrial scenario a standardization of their description is necessary. This work, through a structured review approach, tries to identify commonalities in the research community. From this review it was possible to perceive that most of the industrially focused research work targets simple capabilities like pick and place, the large amount of authors agree on a structure consisting of task, skill and primitive, the Robot Operating System is a common framework both in industrial and non-industrial domains and skills are a main enabler for "high mix -low volume" productions.

show abstract

Section: Trends and Outlooksmentioning

confidence: 99%

Capability-based Frameworks for Industrial Robot Skills: a Survey

Pantano,

Eiband,

Lee

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In contrast to the identification of low confidence task regions to improve the robot's spatial generalization capabilities (Maeda et al, 2017), we focus on the identification of anomalies that can occur in the position and force domain. We introduced our anomaly detection scheme in our previous works and Willibald et al (2020), which is based on a probabilistic action encoding and a statistical outlier detection using the Mahalanobis distance.…”

Section: Autonomous Anomaly Detectionmentioning

confidence: 99%

“…This work gives a more detailed overview of our preliminary study on collaborative programming (Willibald et al, 2020) regarding task representations, evaluates the framework in different applications and adds a user study in order to reveal how people interact with different frameworks.…”

Section: Introductionmentioning

confidence: 99%

Collaborative programming of robotic task decisions and recovery behaviors

et al. 2022

Self Cite

View full text Add to dashboard Cite

Programming by demonstration is reaching industrial applications, which allows non-experts to teach new tasks without manual code writing. However, a certain level of complexity, such as online decision making or the definition of recovery behaviors, still requires experts that use conventional programming methods. Even though, experts cannot foresee all possible faults in a robotic application. To encounter this, we present a framework where user and robot collaboratively program a task that involves online decision making and recovery behaviors. Hereby, a task-graph is created that represents a production task and possible alternative behaviors. Nodes represent start, end or decision states and links define actions for execution. This graph can be incrementally extended by autonomous anomaly detection, which requests the user to add knowledge for a specific recovery action. Besides our proposed approach, we introduce two alternative approaches that manage recovery behavior programming and compare all approaches extensively in a user study involving 21 subjects. This study revealed the strength of our framework and analyzed how users act to add knowledge to the robot. Our findings proclaim to use a framework with a task-graph based knowledge representation and autonomous anomaly detection not only for initiating recovery actions but particularly to transfer those to a robot.

show abstract

“…In many industrial applications, skill parameters are still manually defined [38]. However, recent works consider automatic parameterization techniques, where the skill sequence and skill parameters are defined either by an autonomous planner or extracted from human demonstrations [27], [28]. Also, the complexity of parameters is changing.…”

Section: Trends and Outlooksmentioning

confidence: 99%

Capability-based Frameworks for Industrial Robot Skills: a Survey

Pantano

Eiband

Lee

2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

View full text Add to dashboard Cite

The research community is puzzled with words like skill, action, atomic unit and others when describing robots' capabilities. However, for giving the possibility to integrate capabilities in industrial scenarios, a standardization of these descriptions is necessary. This work uses a structured review approach to identify commonalities and differences in the research community of robots' skill frameworks. Through this method, 210 papers were analyzed and three main results were obtained. First, the vast majority of authors agree on a taxonomy based on task, skill and primitive. Second, the most investigated robots' capabilities are pick and place. Third, industrial oriented applications focus more on simple robots' capabilities with fixed parameters while ensuring safety aspects. Therefore, this work emphasizes that a taxonomy based on task, skill and primitives should be used by future works to align with existing literature. Moreover, further research is needed in the industrial domain for parametric robots' capabilities while ensuring safety.

show abstract

Collaborative Programming of Conditional Robot Tasks

Cited by 5 publications

References 18 publications

Capability-based Frameworks for Industrial Robot Skills: a Survey

Capability-based Frameworks for Industrial Robot Skills: a Survey

Collaborative programming of robotic task decisions and recovery behaviors

Capability-based Frameworks for Industrial Robot Skills: a Survey

Contact Info

Product

Resources

About