# Non-markovian problems/approaches in robotics

As far as i can tell, the markov assumption is quite ubiquitous in probabilistic methods for robotics and i can see why. The notion that you can summarize all of your robot's previous poses with its current pose makes many methods computationally tractable.

I'm just wondering if there are any classic examples of problems in robotics where the markov assumption cannot be used at all. Under what circumstances is the future state of the robot necessarily dependent on the current and at least some past states? In such non-markovian cases, what can be done to alleviate the computational expense? Is there a way to minimize the dependence on previous states to the previous $k$ states, where $k$ can be chosen as small as desired?

• "Under what circumstances is the future state of the robot necessarily dependent on the current and at least some past states?" Such as if the robot were turned off, and moved to a different position before being turned back on? – Mhz4.77 Dec 29 '14 at 1:02