Autonomous Behaviour and Learning in an Uncertain World
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
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Organisations
- University of Oxford (Lead Research Organisation)
- Defence Science and Technology Laboratory (Co-funder)
- Sellafield (United Kingdom) (Co-funder)
- Schlumberger (United Kingdom) (Co-funder)
- United Kingdom Space Agency (Co-funder)
- BAE Systems (United Kingdom) (Co-funder)
- Network Rail (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Lloyd C
(2015)
Variational Inference for Gaussian Process Modulated Poisson Process
in Variational Inference for Gaussian Process Modulated Poisson Process
Calliess, J.
(2013)
Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws.
in WS- Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, ICRA, 2013.
Jan Calliess (Author)
(2013)
Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation.
Jan Calliess (Author)
(2013)
Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws.
Raman D
(2016)
Delineating Parameter Unidentifiabilities in Complex Models
M Hoffman
(2014)
Modular Mechanisms for Bayesian Optimization
Michael Osborne (Author)
(2012)
Active learning of model evidence using Bayesian quadrature
JM Hernandez-Lobato
(2015)
Predictive Enthropy Search for Bayesian Optimization with Unknown Constraints.
M Osborne
(2012)
Active learning of model evidence using Bayesian quadrature.
M.W. Hoffman
(2014)
Bayesian techniques for black box optimization in system identification
Stephen Roberts (Co-Author)
(2012)
Towards optimization-based multi-agent collision avoidance under continuous stochastic dynamics
F. Nyikosa
(2015)
Adaptive Bayesian Optimization for Online Portfolio Selection.
Ahmadi M
(2015)
A convex approach to hydrodynamic analysis
M.P. Deisenroth
(2013)
Gaussian processes for data-efficient learning in robotics and control
Description | 1) Sparse efficient sampling based on informatics criteria can provide stable control algorithms and enable scalable multi-agent coordination. 2) Control mechanisms may be learned from data without a physical mechanism known 3) Guarantees of stability may be derived for probabilistic control methods 4) Bayesian optimisation allows for rapid learning of unknown functions. |
Exploitation Route | via existing industry partners industry partners & academic publication. |
Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Other |
URL | http://www.robots.ox.ac.uk/~parg/aisp |
Description | Our control models have been integrated into drilling simulation by the industrial partner Schlumberger. These models show how, using sparse data, we can effectively use AI techniques to improve reliable control model creation - offering formal some guarantees as well. The extensions of this work can be useful in many areas, from finance to autonomous vehicles. |
First Year Of Impact | 2014 |
Sector | Energy,Financial Services, and Management Consultancy |
Impact Types | Economic |
Title | active sampling for control systems |
Description | sparse observations for active control |
Type Of Material | Computer model/algorithm |
Year Produced | 2013 |
Provided To Others? | No |
Description | Collaboration with Schlumberger |
Organisation | Schlumberger Limited |
Department | Schlumberger Cambridge Research |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | working closely to ensure industrial relevance and disseminate materials |
Collaborator Contribution | providing data and expertise - funding two studentships as a knock on from this project |
Impact | papers, software and know-how |
Start Year | 2012 |