425 search results - page 76 / 85 » Using Stochastic Grammars to Learn Robotic Tasks |
AROBOTS
2011
13 years 2 months ago
2011
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
ATAL
2007
Springer
14 years 1 months ago
2007
Springer
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
NIPS
1998
13 years 8 months ago
1998
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
ICRA
1999
IEEE
13 years 11 months ago
1999
IEEE
Current manufacturing methods for robotic-controlled assembly rely on accurate positioning to ensure task completion, often through the use of special xtures and precise calibrati...