Jethro's Braindump

Uncertainty in Robotics

Robotic applications increasingly deal with more unstructured environments. Robots that can perceive and deal with uncertainty are much more robust in these scenarios.

Uncertainty arises from:

unpredictability of the physical world
limitations in sensor perception
robot actuations involve motors that have control noise
models of the world inherently inaccurate
many algorithms are approximate

A robot that carries a notion of its own uncertainty that acts accordingly is superior to one that does not.

Thrun, Burgard, and Fox, n.d.


  1. Robust, and scale better to complex, unstructured environments
  2. Weaker requirements on the accuracy of the models compared to classical planning algorithms
  3. Sound methodology for many flavours of robot learning
  4. Broadly applicable to many problems, including perception and action


  • Relatively computationally inefficient
  • Requires approximation (exact posteriors are computationally intractable)

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