Difference between revisions of "Cognitive Robotics"

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==Course Aim & Organization==
 
==Course Aim & Organization==
  
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This course addresses the methodological aspects of Cognitive Robotics. Cognitive Robotics is about endowing robots and embodied agents with intelligent behaviour by designing and deploying a processing architecture making them apt to deliberate, learn, and reason about how to behave in response to complex goals in a complex world. Perception and action, and how to model them in neural and symbolic representations are therefore the core issues to address. Inspiring models of Cognitive Robotics arise from different disciplines: the neural architectures from neuroscience, the basic behaviours from ethology, motivations and emotions from psychology, the multirobot behaviour from sociology. Those models could be implemented in terms of formal logic, probabilistic, and neural models turning into embodied computational agents. 
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This course addresses the methodological aspects of Cognitive Robotics. Cognitive Robotics is about endowing robots and embodied agents with intelligent behaviour by designing and deploying a processing architecture making them apt to deliberate, learn, and reason about how to behave in response to complex goals in a complex world. Perception and action, and how to model them in neural and symbolic representations are therefore the core issues to address. Inspiring models of Cognitive Robotics arise from different disciplines: the neural architectures from neuroscience, the basic behaviours from ethology, motivations and emotions from psychology, the multirobot behaviour from sociology. Those models could be implemented in terms of formal logic, probabilistic, and neural models turning into embodied computational agents.
  
 
<!-- Implementation issues are approached and developed into an integrated middleware for robotics, to give students the experience of a quite professional way to develop and experiment robotics algorithms.-->
 
<!-- Implementation issues are approached and developed into an integrated middleware for robotics, to give students the experience of a quite professional way to develop and experiment robotics algorithms.-->
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** the ROS (Robot Operating System) environment for robot simulation and control
 
** the ROS (Robot Operating System) environment for robot simulation and control
 
** robot models and sensor integration in ROS
 
** robot models and sensor integration in ROS
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===Detailed course schedule===
 
===Detailed course schedule===
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  * On Tuesday, in V.S8-A, starts at 08:15, ends at 10:15
 
  * On Tuesday, in V.S8-A, starts at 08:15, ends at 10:15
 
  * On Friday, in V.S8-A, starts at 10:15, ends at 13:15
 
  * On Friday, in V.S8-A, starts at 10:15, ends at 13:15
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{| border="1" align="center" style="text-align:center;"
 
{| border="1" align="center" style="text-align:center;"
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|Date || Day || Time || Room || Teacher || Topic
 
|Date || Day || Time || Room || Teacher || Topic
 
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|06/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Introduction - Fuzzy sets
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|07/03/2017 || Tuesday || 08:15 - 10:15 || V.S8-A || Matteo Matteucci || Course Introduction, Robotics and Cognitive Robotics
 
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|10/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Intro to neural networks and Perceptron model
 
|10/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Intro to neural networks and Perceptron model
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|03/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
|03/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
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|07/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Neural Network exercises
 
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|10/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Applications
 
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|14/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Overfitting limitation
 
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|17/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
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|21/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks
 
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|24/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
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|28/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Inference in Bayesian Networks
 
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|01/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
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|05/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks Demo/Exercises
 
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|12/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinforcement Learning I
 
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|15/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Matteo Matteucci || Bayesian Networks Demo/Exercises
 
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|19/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinfocement Learning – Design
 
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|22/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Reinfocement Learning – Applications
 
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|09/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Evolutionary algorithms – Genetic Algorithms
 
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|12/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Genetic Algorithms – Design
 
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|16/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Genetic Algorithms – Applications
 
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|19/01/2011 || Thursday || 14:15 - 16:15 || E.G.6  || Andrea Bonarini || Hybrid systems
 
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|23/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Closing remarks and exercises
 
 
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===Course Evaluation===
 
===Course Evaluation===
  
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The exam is a test done in regular sessions, starting from the end of the lessons. The test is partitioned in two parts, whose evaluation is averaged. For each of them 32 points are available and a minimum of 15 is needed. The average vote must be greater or equal to 18 to pass the exam. Some example from past years are available below. From year 2011 the format of the exam will change a little bit, but the type of questions will analogous. An example of the format for this year will be published later.
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TBC
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This course can be taken as a stand alone course or as a course integrated with Artificial Intelligence. In both cases, the course will be offered at the same time to students taking one or the other format. The exam will be also the same, but, in the case of integrated course, it will have to be passed together with the exam of Artificial Intelligence, as a unique exam, the same day. The same rules apply for the exam of the integrated course, and the marks obtained in SC and AI will be averaged. The difference between the two solutions is that the integrated course can be selected as a unique course in the study plan.
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==Teaching Material==  
 
==Teaching Material==  

Revision as of 08:49, 7 March 2017


The following are last minute news you should be aware of ;-) 

 01/09/2016: Course will start on second semester of academic year 2016/2017 ... stay tuned!

Course Aim & Organization

This course addresses the methodological aspects of Cognitive Robotics. Cognitive Robotics is about endowing robots and embodied agents with intelligent behaviour by designing and deploying a processing architecture making them apt to deliberate, learn, and reason about how to behave in response to complex goals in a complex world. Perception and action, and how to model them in neural and symbolic representations are therefore the core issues to address. Inspiring models of Cognitive Robotics arise from different disciplines: the neural architectures from neuroscience, the basic behaviours from ethology, motivations and emotions from psychology, the multirobot behaviour from sociology. Those models could be implemented in terms of formal logic, probabilistic, and neural models turning into embodied computational agents.


Teachers

The course is composed by a blending of theory and practice lectures from the course teacher and the teaching assistants (in order of appearance):

Course Program and Teaching Material

The course comprises theoretical lectures (30h regarding 1-3) and practical sessions (20h regarding 4-5):

  • Cognitive Robotics introduction
    • Cognition and the sense-plan-act architecture
    • Deliberative, reactive, and hybrid approaches
  • Deliberative systems for cognitive robots
    • Symbolic planning and PDDL
  • Bioinspired controllers for autonomous robots
    • Behavior based architectures
    • Neural networks and learning
  • Human-Robot interaction
    • Natural language processing
    • Non verbal human robot interaction
    • (Deep) learning for vision/nlp/control …


Detailed course schedule

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (they will be notified to you by email). 

Note: Lecture timetable interpretation
* On Tuesday, in V.S8-A, starts at 08:15, ends at 10:15
* On Friday, in V.S8-A, starts at 10:15, ends at 13:15
Date Day Time Room Teacher Topic
07/03/2017 Tuesday 08:15 - 10:15 V.S8-A Matteo Matteucci Course Introduction, Robotics and Cognitive Robotics
10/10/2011 Monday 15:15 - 17:15 S.1.3 Matteo Matteucci Intro to neural networks and Perceptron model
13/10/2011 Thursday 14:15 - 16:15 E.G.6 Andrea Bonarini Fuzzy sets
17/10/2011 Monday 15:15 - 17:15 S.1.3 Matteo Matteucci Hebbian learning, the xor problem, from perceptron to backpropagation
20/10/2011 Thursday 14:15 - 16:15 E.G.6 Andrea Bonarini Fuzzy logic
24/10/2011 Monday 15:15 - 17:15 S.1.3 Matteo Matteucci Feedforward topologies and Backpropagation
27/10/2011 Thursday 14:15 - 16:15 E.G.6 Andrea Bonarini Fuzzy rules - design of fuzzy systems
03/11/2011 Thursday 14:15 - 16:15 --- --- No lecture today

Course Evaluation

TBC

Teaching Material

The course material comprises slides from the teachers and scientific literature, both provided in the following.

Teacher Slides

In the following you can find the lecture slides used by the teacher and the teaching assistants during classes:

  • ...

Books and Papers

  • ...

Useful Links

...

  • ...
  • ...

Exam Samples and Results

Not yet existing

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