Michael James 0001

IROS Conference 2008 Conference Paper

Local line segments as primitives for scene understanding

• Michael James 0001
• Dmitri Dolgov

With the increasing sophistication of sensors such as 3D laser range finders and their use in more complex environments, new approaches for the efficient processing of 2 and 3D point data are becoming more and more important. We develop algorithms for finding and using line segments as primitives for processing such data, and explore the benefits of such a choice. Line segments are simple enough to allow for relatively efficient detection algorithms, but are sophisticated enough to support tracking of dynamic obstacles over time. Further, such a parametric representation of the environment is better suited to deal with the noise inherent in dynamic, real-world environments. We develop a locally-adaptive line-detection algorithm, the output of which is used in a line-segment tracker. The resulting representation is shown to alleviate some of the problems in ray-tracing a dynamic obstacle map.

ICML Conference 2006 Conference Paper

Learning predictive state representations using non-blind policies

• Michael H. Bowling
• Peter McCracken
• Michael James 0001
• James Neufeld
• Dana F. Wilkinson

ICML Conference 2005 Conference Paper

Learning predictive state representations in dynamical systems without reset

• Britton Wolfe
• Michael James 0001
• Satinder Singh 0001

ICML Conference 2004 Conference Paper

Learning and discovery of predictive state representations in dynamical systems with reset

• Michael James 0001
• Satinder Singh 0001

UAI Conference 2004 Conference Paper

Predictive State Representations: A New Theory for Modeling Dynamical Systems

• Satinder Singh 0001
• Michael James 0001
• Matthew R. Rudary

Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs) are a recently introduced class of models for discrete-time dynamical systems. The key idea behind PSRs and the closely related OOMs (Jaeger's observable operator models) is to represent the state of the system as a set of predictions of observable outcomes of experiments one can do in the system. This makes PSRs rather different from history-based models such as nth-order Markov models and hidden-state-based models such as HMMs and POMDPs. We introduce an interesting construct, the systemdynamics matrix, and show how PSRs can be derived simply from it. We also use this construct to show formally that PSRs are more general than both nth-order Markov models and HMMs/POMDPs. Finally, we discuss the main difference between PSRs and OOMs and conclude with directions for future work.