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Representing and Learning Complex Object Interactions.

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Robots can learn indirect control of objects through intermediate steps, like using a steering wheel to drive. This framework uses chains of Markov decision processes for complex robotic interactions.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Theory

Background:

  • Robots often need to control objects indirectly through intermediary objects.
  • Complex interactions require sophisticated control strategies beyond direct manipulation.

Purpose of the Study:

  • To present a framework for representing and learning indirect object control in robotics.
  • To enable robots to achieve goals via a sequence of actions on intermediate objects.

Main Methods:

  • Formalizing indirect control as chains of Markov decision processes (MDPs).
  • Utilizing learning from demonstration to acquire control policies.
  • Developing algorithms for learning and executing indirect control strategies.

Main Results:

  • Demonstrated successful indirect control in two distinct robotic systems.
  • Showcased the framework's ability to handle complex object interactions.
  • Validated the effectiveness of learning from demonstration for indirect control tasks.

Conclusions:

  • The proposed framework effectively models and enables indirect robotic control.
  • Chains of MDPs provide a viable approach for learning complex interaction policies.
  • Learning from demonstration is a practical method for robots to acquire indirect manipulation skills.