Probabilistic inference for structured planning in robotics
Marc Toussaint and Christian Goerick
In: Int Conf on Intelligent Robots and Systems (IROS 2007), Nov 2007, San Diego.
Real-world robotic environments are highly structured.
The scalability of planning and reasoning methods to
cope with complex problems in such environments crucially
depends on exploiting this structure. We propose a new approach
to planning in robotics based on probabilistic inference.
The method uses structured Dynamic Bayesian Networks to
represent the scenario and efficient inference techniques (loopy
belief propagation) to solve planning problems. In principle,
any kind of factored or hierarchical state representations can
be accounted for. We demonstrate the approach on reaching
tasks under collision avoidance constraints with a humanoid
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Marc Toussaint|
|Deposited On:||25 February 2008|