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Sensorless Pose Determination Using Randomized Action Sequences.

Pragna Mannam1, Alexander Volkov1, Robert Paolini1

  • 1The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

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Randomized actions can achieve precise 2D object manipulation without sensing or planning. Long sequences of random actions reduce object pose uncertainty, leading to a determined final pose.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Robotic manipulation typically requires sensing and planning for object reorientation.
  • Prior work by Erdmann and Mason demonstrated sensorless reorientation using Newtonian mechanics.

Purpose of the Study:

  • To investigate the effects of unplanned, randomized action sequences on 2D object pose uncertainty.
  • To determine if random actions can achieve a determined object pose without explicit planning or sensing.

Main Methods:

  • Simulations and real robot experiments were conducted.
  • Analysis of randomized action sequences on 2D object pose uncertainty.
  • Exploration of varying object shapes, action sequences, and surface friction.

Main Results:

  • Long enough sequences of random actions can converge to a determined object pose.
  • Randomized action sequences were shown to reduce the entropy of the object pose distribution.
  • The study explored the influence of object geometry, action parameters, and friction on the outcome.

Conclusions:

  • Sensorless, unplanned random actions offer a viable alternative for achieving deterministic 2D object manipulation.
  • This approach demonstrates a method for reducing pose uncertainty through stochastic processes in robotics.