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AROBOTS
2010
128views more  AROBOTS 2010»
15 years 4 months ago
Track-based self-supervised classification of dynamic obstacles
Abstract This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that r...
Roman Katz, Juan Nieto, Eduardo Mario Nebot, Bertr...
EH
1999
IEEE
351views Hardware» more  EH 1999»
15 years 8 months ago
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
NIPS
1994
15 years 5 months ago
Catastrophic Interference in Human Motor Learning
Biological sensorimotor systems are not static maps that transform input sensory information into output motor behavior. Evidence from many lines of research suggests that their r...
Tom Brashers-Krug, Reza Shadmehr, Emanuel Todorov
NIPS
1996
15 years 5 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
ICCV
2009
IEEE
16 years 9 months ago
Moving in Stereo: Efficient Structure and Motion Using Lines
We present a fast and robust system for estimating structure and motion using a stereo pair, with straight lines as features. Our first set of contributions are efficient algorit...
Manmohan Chandraker, Jongwoo Lim, David Kriegman