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» Learning to Drive and Simulate Autonomous Mobile Robots
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ICRA
2002
IEEE
105views Robotics» more  ICRA 2002»
14 years 18 days ago
Learning Behavioral Parameterization using Spatio-Temporal Case-Based Reasoning
This paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It is ba...
Maxim Likhachev, Michael Kaess, Ronald C. Arkin
ACL
2009
13 years 5 months ago
Learning a Compositional Semantic Parser using an Existing Syntactic Parser
We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form). Unlike previous methods, it exploits an existing syntact...
Ruifang Ge, Raymond J. Mooney
ICRA
2010
IEEE
220views Robotics» more  ICRA 2010»
13 years 6 months ago
Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study
— Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceanographic research community. Contrary to traditional moored or static pl...
Ryan N. Smith, Arvind Pereira, Yi Chao, Peggy Li, ...
JFR
2006
140views more  JFR 2006»
13 years 7 months ago
Improving robot navigation through self-supervised online learning
In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations...
Boris Sofman, Ellie Lin, J. Andrew Bagnell, John C...
ICTAI
2006
IEEE
14 years 1 months ago
Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents
In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approx...
Daniel Stronger, Peter Stone