Monthly Archives: April 2019

You are browsing the site archives by month.

Designing robotic architectures by coordinating different modules in a data-flow graphical paradigm

April 8, 2019 16:29 ,

Sebastian Buck, Andreas Zell, CS::APEX: A Framework for Algorithm Prototyping and Experimentation with Robotic Systems. Modeling Perception and High Level Robot Control with Activity Flow Graphs, Journal of Intelligent & Robotic Systems (2019) 94:371–387, DOI: 10.1007/s10846-018-0831-7.

Robotic systems differ drastically in their sensory capabilities, their computational power and their designated tasks. For
efficient algorithm development, however, we need to have a common modeling framework that enables us to generalize and
re-use existing solutions. A modular approach, which is coherent across different platforms, also allows faster prototyping
of new systems, given that existing functionality can be reused from already implemented modules. In this paper we develop
a modeling framework based on data flow graphs that achieves the following goal: We first merge synchronous data flow
and reactive programming into hybrid flow graphs, where we explicitly model synchronous and asynchronous data flow.
Then we transfer concepts from finite-state machines to achieve a coherent framework which we call Activity Flow Graphs.
The flow of activity enables us to model high level states directly in the data flow graph. The result is a single computation
graph that can express both perception and high level control aspects of any robotic system. This theoretical foundation is
the core of our open-source software framework CS::APEX, which allows the creation, manipulation and evaluation of
Activity Flow Graphs and enables rapid prototyping and experimentation and can be used with any robot supporting the
Robot Operating System (ROS). We then demonstrate the framework with two high level models for a fetch-and-delivery
robot and a person following robot.

Posted in: Robotic architectures, Robotic software , Tagged: ,

Learning multiple-factors metrics for measuring the similarity between objects

April 8, 2019 16:22 ,

H. Ye, D. Zhan, Y. Jiang and Z. Zhou, What Makes Objects Similar: A Unified Multi-Metric Learning Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 5, pp. 1257-1270, DOI: 10.1109/TPAMI.2018.2829192.

Linkages are essentially determined by similarity measures that may be derived from multiple perspectives. For example, spatial linkages are usually generated based on localities of heterogeneous data. Semantic linkages, however, can come from even more properties, such as different physical meanings behind social relations. Many existing metric learning models focus on spatial linkages but leave the rich semantic factors unconsidered. We propose a Unified Multi-Metric Learning (Um$^2$2l) framework to exploit multiple types of metrics with respect to overdetermined similarities between linkages. In Um$^2$2l, types of combination operators are introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages. Besides, we propose a uniform solver for Um$^2$2l, and the theoretical analysis reflects the generalization ability of Um$^2$2l as well. Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of Um$^2$2l. Visualization results also validate its ability to physical meanings discovery.

Posted in: Computer vision , Tagged:

Taking into account the influence of a recommender in the change of behaviour of the agent using it

April 3, 2019 08:37 ,

Jonathan P. Epperlein, Sergiy Zhuk, Robert Shorten, Recovering Markov models from closed-loop data, Automatica, Volume 103, 2019, Pages 116-125, DOI: 10.1016/j.automatica.2019.01.022.

Situations in which recommender systems are used to augment decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders) are created with a view to affecting behavioural change. Clearly, successful applications actuating behavioural change, affect the original model underpinning the predictor, leading to an inconsistency. This feedback loop is often not considered in standard machine learning techniques which rely upon machine learning/statistical learning machinery. The objective of this paper is to develop tools that recover unbiased user models in the presence of recommenders. More specifically, we assume that we observe a time series which is a trajectory of a Markov chain R modulated by another Markov chain S, i.e. the transition matrix of R is unknown and depends on the current state of S. The transition matrix of the latter is also unknown. In other words, at each time instant, S selects a transition matrix for R within a given set which consists of known and unknown matrices. The state of S, in turn, depends on the current state of R thus introducing a feedback loop. We propose an Expectation–Maximisation (EM) type algorithm, which estimates the transition matrices of S and R. Experimental results are given to demonstrate the efficacy of the approach.

Posted in: Control Engineering , Tagged: