Difference between revisions of "Cognitive Robotics"
(→Course Aim & Organization) |
|||
| Line 6: | Line 6: | ||
==Course Aim & Organization== | ==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. | + | 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.--> | ||
===Teachers=== | ===Teachers=== | ||
| − | The course is composed by a blending of | + | The course is composed by a blending of theory and practice lectures from the course teacher and the teaching assistants (in order of appearance): |
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the teacher | * [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the teacher | ||
| − | * | + | * Roberto Basilli |
| − | * | + | * Andrea Bonarini |
| + | * Marco Ciccone | ||
===Course Program and Teaching Material=== | ===Course Program and Teaching Material=== | ||
| Line 20: | Line 23: | ||
The course comprises theoretical lectures (30h regarding 1-3) and practical sessions (20h regarding 4-5): | 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 … | ||
| + | |||
| + | <!-- | ||
* Deliberative systems for cognitive robots | * Deliberative systems for cognitive robots | ||
** reactive, cognitive, hybrid architectures | ** reactive, cognitive, hybrid architectures | ||
** discrete and continuous planners: action representation and map representation | ** discrete and continuous planners: action representation and map representation | ||
** non linear planners, partial order planners, and random planners | ** non linear planners, partial order planners, and random planners | ||
| − | |||
* Bioinspired controllers for autonomous robots | * Bioinspired controllers for autonomous robots | ||
** neural controllers and neural models of space and paths | ** neural controllers and neural models of space and paths | ||
** learning mechanisms in robot and embodied agents | ** learning mechanisms in robot and embodied agents | ||
** deep learning architecture for action and perception | ** deep learning architecture for action and perception | ||
| − | |||
* Probabilistic Robotics | * Probabilistic Robotics | ||
** localization and mapping: probabilistic models, sensor models, SLAM | ** localization and mapping: probabilistic models, sensor models, SLAM | ||
** action and learning: markov decision processes, POMDP, reinforcement learning | ** action and learning: markov decision processes, POMDP, reinforcement learning | ||
| − | |||
* User/robot and robot/robot interaction | * User/robot and robot/robot interaction | ||
** multisensorial interfaces: physical principles and telecontrol. | ** multisensorial interfaces: physical principles and telecontrol. | ||
** the environment as a communication medium: distributed sensing for robot/robot interaction. | ** the environment as a communication medium: distributed sensing for robot/robot interaction. | ||
** interface to interact with real and virtual worlds. | ** interface to interact with real and virtual worlds. | ||
| − | |||
* Middleware and integration | * Middleware and integration | ||
** 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 | ||
| + | !>> | ||
===Detailed course schedule=== | ===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). | 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 | ||
<!-- | <!-- | ||
| + | |||
{| border="1" align="center" style="text-align:center;" | {| border="1" align="center" style="text-align:center;" | ||
|- | |- | ||
Revision as of 08:46, 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!
Contents
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):
- Matteo Matteucci: the teacher
- Roberto Basilli
- Andrea Bonarini
- Marco Ciccone
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 …
Course Evaluation
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.
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.
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
