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Published on: November 2, 2012
Modeling and Learning Constraints for Creative Tool Use.
Tesca Fitzgerald1, Ashok Goel2, Andrea Thomaz3
1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
Robots can learn to improvise tool use for novel tasks by identifying and evaluating potential tools. A learning by correction method, using human feedback, enables robots to adapt tool usage effectively.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Human creativity involves improvisation, especially in tool use when expected tools are unavailable.
- Robots require advanced tool-using skills to adapt to real-world task variations.
Purpose of the Study:
- To present a process for robots to improvise tool use.
- To enable robots to identify and adapt to unknown task constraints for successful task completion.
Main Methods:
- A high-level process for tool improvisation: identification, evaluation, and adaptation.
- Highlighting the role of tooltips in tool-task pairing.
- Implementing a learning by correction method with human teacher feedback.
Main Results:
- Demonstrated efficacy of the learning by correction method on a physical robot.
- Showcased successful within-task and across-task transfer of learned tool improvisation skills.
Conclusions:
- Robots can learn to creatively adapt tool use through a structured improvisation process.
- Learning by correction is an effective method for robots to acquire unknown task constraints and generalize tool use.

