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IROS
2007
IEEE
123views Robotics» more  IROS 2007»
14 years 1 months ago
Learning humanoid reaching tasks in dynamic environments
— A central challenging problem in humanoid robotics is to plan and execute dynamic tasks in dynamic environments. Given that the environment is known, sampling-based online moti...
Xiaoxi Jiang, Marcelo Kallmann
CA
1999
IEEE
13 years 12 months ago
Fast Synthetic Vision, Memory, and Learning Models for Virtual Humans
This paper presents a simple and efficient method of modeling synthetic vision, memory, and learning for autonomous animated characters in real-time virtual environments. The mode...
James J. Kuffner Jr., Jean-Claude Latombe
AAAI
2008
13 years 10 months ago
An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure
When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster b...
Masahiro Ono, Brian C. Williams
COMPGEOM
2008
ACM
13 years 9 months ago
Maximum thick paths in static and dynamic environments
We consider the problem of finding a maximum number of disjoint paths for unit disks moving amidst static or dynamic obstacles. For the static case we give efficient exact algorit...
Esther M. Arkin, Joseph S. B. Mitchell, Valentin P...
AGENTS
1998
Springer
13 years 11 months ago
Learning Situation-Dependent Costs: Improving Planning from Probabilistic Robot Execution
Physical domains are notoriously hard to model completely and correctly, especially to capture the dynamics of the environment. Moreover, since environments change, it is even mor...
Karen Zita Haigh, Manuela M. Veloso