Abstracting from Robot Sensor Data using Hidden Markov Models

ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003-4610 February 1, 1999 This work is the rst step of a larger e ort aimed at learninglogical descriptionsfrom robot sensory data. These representations are more compact than sensory traces and will support logical reasoning. We view the robot's experiences as trajectories through an unknown state space. The robot receives information about the state of the world through its sensors. We present a technique to automatically extract atomic propositions from these sensors. Our assumption is that a state means that something is invariant in the world, and that this invariance is re ected in some constant sensor values, or constant functions of sensor values. Our task is then to nd the states and their invariant characterization. We employ a hidden Markov model to nd the states and their distributional charact...

• Type: Conference
• Year: 1999
• Where: ICML
• Authors: Laura Firoiu, Paul R. Cohen
• Keywords: Atomic Propositions, Constant Sensor Values, ICML 1999, Machine Learning, Robot Sensor Data