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Representing, learning, and controlling complex object interactions.

Yilun Zhou1, Benjamin Burchfiel2, George Konidaris3

  • 11Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA.

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Summary
This summary is machine-generated.

Robots can learn complex indirect control by modeling object interactions as Markov decision processes (MDPs). This framework enables robots to manipulate objects through intermediate tools, like using a joystick to play games.

Keywords:
Markov decision processRoboticsTask learningTask representation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Theory

Background:

  • Robots often face scenarios requiring indirect control of target objects via intermediate mechanisms.
  • Reasoning about multi-object relationships is crucial for complex manipulation tasks.

Purpose of the Study:

  • To develop a framework for representing and learning complex object interactions for robots.
  • To enable robots to achieve indirect control over objects through intermediate interfaces.

Main Methods:

  • Formalizing interactions as chains and graphs of Markov decision processes (MDPs).
  • Learning interaction models from data.
  • Collapsing complex models into a single MDP for optimal policy computation.
  • Developing efficient planning algorithms for large state spaces.

Main Results:

  • Demonstrated the ability to learn models of indirect object manipulation.
  • Showcased a method to solve complex interaction systems by reducing them to a single MDP.
  • Introduced a planning algorithm for efficient policy generation in large-scale systems.

Conclusions:

  • The proposed framework effectively models and controls complex indirect robotic interactions.
  • Learning from demonstration enables robots to master tasks requiring manipulation through intermediate objects.
  • This approach is applicable to diverse tasks, including game control and operating dispensers.