Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Feature selection is fundamental to knowledge discovery from massive amount of high-dimensional data. In an effort to establish theoretical justification for feature selection al...
— In this work, we have extended the concept of constrained motion control of robots to surgical tasks that require multiple robots. We present virtual fixtures to guide the mot...
In reactive routing protocols, active routes for multihop connections retain their topological structure in spite of node movement over time. Unfortunately, node movements may mak...
This paper proposes two techniques for low-complexity rate-distortion (R-D) optimized streaming of packetized video. These techniques enable computing packet transmission schedule...
Jacob Chakareski, John G. Apostolopoulos, Bernd Gi...