Autonomous behaviour and learning in an uncertain world

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

The key challenges facing research, development and deployment of autonomous systems require principled solutions in order for scalable systems to become viable. This proposal intertwines probabilistic (Bayesian) inference, model-predictive control, distributed information networks, human-in-the-loop and multi-agent systems to an unprecedented degree. The project focuses on the principled handling of uncertainty for distributed modelling in complex environments which are highly dynamic, communication poor, observation costly and time-sensitive. We aim to develop robust, stable, computationally practical and principled approaches which naturally accommodate these real-world challenges.

Our proposed framework will enable significant progress to be made in a large number of areas essential to intelligent autonomous systems, including 1) the assessment of reliability and fusion of disparate sources of data, 2) allow active data selection based on Bayesian sequential decision making under realistic time, information & computation constraints, 3) allow the advancement of Bayesian reinforcement algorithms in complex systems, and 4) extend Model predictive control (MPC) to probabilistic settings using Gaussian process non-parametric models.

At the systems level, these developments will permit the design of overarching methods for 1) controlled autonomous systems which interact and collaborate, 2) integration of sensing, inference, decision making and learning in acting systems and 3) design methods for validation and verification of systems to enhance robustness and safety.

The ability to meet these objectives depends on a multitude of recent technical developments. These include, 1) development of practical non-parametric algorithms for on-line learning and adaptation 2) approximate inference for Bayesian sequential decision making under constraints, 3) the development of sparse data selection and sparse representation methods for practical handling of large data sets with complex decentralised systems and 4) the implementation of and deployment on powerful modern parallel architectures such as GPUs.

We aim to build on our expertise in Bayesian machine learning, multi-agent systems and control theory and by drawing together closely related developments in these complementary fields we will be able to make substantial improvements to the way artificial agents are able to learn and act, combine and select data sources intelligently, and integrate in robust ways into complex environments with multiple agents and humans in the loop.

Planned Impact

Autonomous intelligent systems are expected to have an increasing effect on our lives. Initially they will be used within highly technical systems, such as robots for handling radioactive materials in power stations, for performing military reconnaissance without risking soldiers' lives, for helping surgeons, or for excavating building sites. Later they are likely to become used in situations which involve interaction with the general public, for example to provide assistance to the elderly or disabled, or perhaps to take over the driving of cars.

In order to allow any of these things to be done successfully and safely, research is needed in some underpinning engineering and science. The biggest challenge is that of dealing with the huge amount of uncertainty that will be faced by autonomous intelligent systems - uncertainty that humans can often deal with pretty well, but that we can still handle to only a limited degree in our mathematics and engineering. Therefore the scientific core of our project rests on the use of probability theory and in particular on the use of Bayes' theorem, and a whole family of methods that result from it, for calculating and keeping track of uncertainty in very complex situations. This is necessary if autonomous agents are to act intelligently in non-trivial situations.

Many people have already done plenty of work on 'Bayesian methods', so what is new in this project? The novelty is the use of these methods as a 'glue' to tie together the many and various technologies that are needed to make autonomous intelligent systems work. Some of these (such as flying an aeroplane) are well known and can achieve great precision; others (such as distinguishing a pedestrian in the road from a shadow, or an underground cable from a rock) are still at a relatively early stage of evolution. A key capability required of autonomous intelligent systems is that of planning, and re-planning in the face of incoming evidence and measurements, how to perform or complete a task. This requires some understanding of behaviours, both of one's own system (eg 'how does this aeroplane work?') and of one's environment (eg 'where will that storm go next?'). The methods currently used to represent and analyse these different aspects differ a lot in their mathematical forms. Our ambition is to develop methods, with a Bayesian basis, which will allow them all to be used with each other.

Our project will investigate in detail how to integrate such technologies in a way that enables them all to work together. In addition to discovering how to do this, we will also train a number of researchers in the required techniques, spread the word to other academic researchers, and share our results with a number of companies who are trying to develop autonomous intelligent systems.

Publications

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Deisenroth MP (2015) Gaussian Processes for Data-Efficient Learning in Robotics and Control. in IEEE transactions on pattern analysis and machine intelligence

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Hernández-Lobato JM (2016) A General Framework for Constrained Bayesian Optimization using Information-based Search in Journal of Machine Learning Research

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Schön T (2017) Using Inertial Sensors for Position and Orientation Estimation in Foundations and Trends® in Signal Processing

 
Description We have developed new machine learning algorithms which can learning from noisy data. Special focus has been on approximate algorithms which can handle very large amounts of data and on Reinforcement Learning algorithms which can learn to act in uncertain, and unknown environments.
Exploitation Route Better understanding of machine learning algorithms and better, more practical implementations of machine learning algorithms are an urgent requirement for widespread use of these modern techniques in science and engineering.
Sectors Digital/Communication/Information Technologies (including Software),Other

 
Description A key challenge in the widespread application of autonomous systems is the representation and reasoning in the presence of uncertainty. This project aims to tackle a number of challenging problems including modelling, adaptation, prediction, control and decision making in data rich environments using limited computational resources. These approaches all rely on probabilities to represent and reason about uncertain environments. The key outcomes are development of new computational methodologies, better understanding of methods, and the development of software implementations of the methods. In this project we have a close collaboration with Schlumberger Research; together we are exploring the applicability of these data driven methods from machine learning and model predictive control, in real, challenging noisy and uncertain environments met in the drilling (for oil and gas) industry.
First Year Of Impact 2013
Sector Digital/Communication/Information Technologies (including Software),Education,Energy
Impact Types Economic

 
Description Arno Solin 
Organisation Aalto University
Country Finland 
Sector Academic/University 
PI Contribution Research collaboration, combining our expertise, mainly focusing on indoor localisation using magnetometers.
Collaborator Contribution Research collaboration, combining our expertise, mainly focusing on indoor localisation using magnetometers.
Impact Submission of two papers: 1) Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön and Simo Särkkä, Modeling and interpolation of the ambient magnetic field by Gaussian processes. Conditionally accepted to IEEE Transactions on Robotics, 2017. 2) Manon Kok and Arno Solin, Rank-reduced Gaussian process maps for magnetic field SLAM, 2018.
Start Year 2015
 
Description Schlumberger 
Organisation Schlumberger Limited
Department Schlumberger Cambridge Research
Country United Kingdom 
Sector Academic/University 
PI Contribution Research into the use of probabilistic methods in model predictive control and machine learning to help understand and control aspects of drilling. Autonomous control is critical as communication in deep wells is very poor. Modelling aspects include the movement of the drill string and the management of well pressure.
Collaborator Contribution Sharing of tools and data recorded from actual wells, test wells and simulation.
Impact This is an ongoing feasibility study in the application of sophisticated probabilistic methods from model predictive control and machine learning.
Start Year 2012
 
Title Gaussian Process for Machine Learning (GPML) Toolbox, version 4.2 
Description Gaussian Processes (GPs) can conveniently be used for Bayesian supervised learning, such as regression and classification. In its simplest form, GP inference can be implemented in a few lines of code. However, in practice, things typically get a little more complicated: you might want to use complicated covariance functions and mean functions, learn good values for hyperparameters, use non-Gaussian likelihood functions (rendering exact inference intractable), use approximate inference algorithms, or combinations of many or all of the above. This is what the GPML software package does. 
Type Of Technology Software 
Year Produced 2010 
Open Source License? Yes  
Impact GPML is a free toolbox used widely across the education and industrial sectors. 
URL http://www.gaussianprocess.org/gpml/code
 
Description ERNSI presentation 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other academic audiences (collaborators, peers etc.)
Results and Impact ERNSI is the European Research Network on System Identification. This was a presentation for the network that provided an overview of our approach to Bayesian optimization and engendered discussion of these methods as well as input from the wide system identification community (in Europe).

There was a good deal of discussion as well as input on applications from the system identification community.
Year(s) Of Engagement Activity 2014
 
Description ERNSI workhshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Two poster presentations at the yearly ERNSI workshop on System Identification
Year(s) Of Engagement Activity 2017
URL https://ernsi2017.sciencesconf.org/
 
Description Guest lectures at TU Berlin 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Undergraduate students
Results and Impact Two guest lectures in the master course Inertial Sensor Fusion at the TU Berlin
Year(s) Of Engagement Activity 2018
URL http://www.control.tu-berlin.de/Teaching:Inertial_Sensor_Fusion
 
Description Invited talk at the University of Sheffield 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact Talk at the University of Sheffield, invited by Prof. Lyudmila Mihaylova
Year(s) Of Engagement Activity 2017
 
Description RSS Invited talk 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Other academic audiences (collaborators, peers etc.)
Results and Impact This was a presentation of my work on Bayesian optimization to the Royal Statistical Society's annual meeting. The talk encouraged a great deal of conversation and discussion.

The talk introduced our particular approaches and gave a preliminary view of software implementing our techniques.
Year(s) Of Engagement Activity 2014