Collaborative programming of robotic task decisions and recovery behaviors

Thomas Eiband, Christoph Willibald, Isabel Tannert, Bernhard Weber, Dongheui Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)229-247
Number of pages19
JournalAutonomous Robots
Issue number2
StatePublished - Feb 2023
Externally publishedYes


  • Anomaly detection
  • Collaborative programming
  • Conditional task
  • Execution monitoring
  • Force-based tasks
  • Interactive programming
  • Learning from demonstration
  • Programming by demonstration
  • Recovery behavior
  • Task-graph


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