Learning task specific plans through sound and visually interpretable demonstrations

Autonomous robots operating in human environments will need to automatically learn to perform new tasks without requiring the implementation of task-specific actions or time-consuming deliberative planning at run-time. In this work, we contribute a demonstration-based approach for teaching a robot task-specific planners involving complex sequential tasks with repetitions. Complexity of tasks results from step repetitions, execution failures and conditionally executing plans. Our demonstration approach uses sound and visually interpretable cues to guide and indicate the various actions and objects to a robot. The robot in turn performs the actions and generalizes its execution into a task-specific planner. We demonstrate the successful plan learning for two different tasks implemented in real-world settings.

Authors

• Harini Veeraraghavan
• Manuela Veloso

Keywords

• Robots
• Visualization
• Object recognition
• Uncertainty
• Humans
• Robot sensing systems
• Planning
• Preconditioning
• Sequence Of Actions
• Resting-state
• Single Activity
• Learning Phase
• Application Conditions
• Objective Conditions
• Object Identification
• Partial Order
• Robot Motion
• Relevant Objects
• Sequential Execution