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| + | #REDIRECT [[Cognitive Robotics Lecture Plan]] |
| − | {| class="wikitable"
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| − | |- style="vertical-align: top;"
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| − | ! Week
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| − | ! Lecture
| + | |
| − | ! Topic
| + | |
| − | ! Material covered
| + | |
| − | ! Required hardware
| + | |
| − | ! Required software
| + | |
| − | ! Pre-class reading
| + | |
| − | ! Homework exercises
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| − | |- style="vertical-align: top;"
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| − | | 1
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| − | | 1
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| − | | Cognitive robotics
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| − | | Introduction to AI and cognition in robotics. Industrial requirements. Artificial cognitive systems. Cognitivist, emergent, and hybrid paradigms in cognitive science. Autonomy.
| + | |
| − | | None
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| − | | None
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| − | | Vernon (2014), Chapters 1, 2, and 4.
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| − | | Installation of software tools.
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| − | |- style="vertical-align: top;"
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| − | | 1
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| − | | 2
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| − | | Robot vision I
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| − | | Optics, sensors, and image formation. Image acquisition. Image filtering. Edge detection.
| + | |
| − | | USB camera
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| − | | OpenCV
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| − | | Kragic and Vincze (2010). Szeliski (2010), Sections 1.1, 1.2, 2.3, 3.2, 4.2. Vernon (1991), Sections 2.1, 2.1
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| − | |Image acquisition and image processing using OpenCV
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| − | |- style="vertical-align: top;"
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| − | | 2
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| − | | 3
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| − | | Robot vision II
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| − | | Segmentation. Hough transform: line, circle, and generalized transform; extension to codeword features. Colour-based segmentation.
| + | |
| − | | USB camera
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| − | | OpenCV
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| − | | Szeliski (2010), Sections 3.1.2, 3.3.4, 4.3.2. Vernon (1991), Section 3.1, 3.2, 3.3, 4.2.1, 4.2.2, 5.3, 6.4.
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| − | | Hough transforms and colour segmentation using OpenCV
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| − | |- style="vertical-align: top;"
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| − | | 2
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| − | | 4
| + | |
| − | | Robot vision III
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| − | | Object recognition. Interest point operators. Gradient orientation histogram - SIFT descriptor. Colour histogram intersection. Haar features, boosting, face detection.
| + | |
| − | | USB camera
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| − | | OpenCV, Vienna University of Technology BLORT Library
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| − | | Szeliski (2010), Sections 4.1.2, 4.1.3, 4.1.4, 4.1.5, 14.1.1.
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| − | | Face detection and object recognition using OpenCV
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| − | |- style="vertical-align: top;"
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| − | | 3
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| − | | 5
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| − | |Robot vision IV
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| − | |Homogeneous coordinates and transformations. Perspective transformation. Camera model and inverse perspective transformation. Stereo vision. Epipolar geometry. Structured light & RGB-D cameras.
| + | |
| − | |USB camera
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| − | |OpenCV
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| − | |Szeliski (2010), Sections 2.1, 11.1, 11.2, 11.3. Vernon (1991), Section 8.6, 9.4.2.
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| − | |Camera calibration
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| − | |- style="vertical-align: top;"
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| − | | 3
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| − | | 6
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| − | |Robot vision V
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| − | |Visual attention. Plane pop-out. RANSAC. Differential geometry. Surface normals and Gaussian sphere. Point clouds. 3D descriptors.
| + | |
| − | |Kinect RGB-D sensor
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| − | |Vienna University of Technology RGB-D Segmentation Library and V4R Library
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| − | |Szeliski (2010), Sections 12.4. Point Cloud Library tutorial.
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| − | |Analysis of point cloud data from RGB-D camera
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| − | |- style="vertical-align: top;"
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| − | | 4
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| − | | 7
| + | |
| − | |Mobile robots I
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| − | |Differential drive locomotion. Forward and inverse kinematics. Holonomic and non-holonomic constraints. Cozmo mobile robot.
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| − | |Anki Cozmo mobile robot
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| − | |Anki Cozmo SDK, OpenCV
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| − | |Python tutorial. Cozmo SDK API. OpenCV Python tutorial.
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| − | |Cozmo locomotion (e.g. program Cozmo to drive along a pre-determined route and perform face detection)
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| − | |- style="vertical-align: top;"
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| − | | 4
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| − | | 8
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| − | |Mobile robots II
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| − | |Map representation. Probabilistic map-based localization. Landmark-based localization.
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| − | |Anki Cozmo mobile robot
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| − | |Anki Cozmo SDK, OpenCV
| + | |
| − | |Python tutorial. Cozmo SDK API. OpenCV Python tutorial.
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| − | |Cozmo landmark recognition
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| − | |- style="vertical-align: top;"
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| − | | 5
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| − | | 9
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| − | |Mobile robots III
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| − | |SLAM: simultaneous localization and mapping. Extended Kalman Filter (EKF) SLAM. Visual SLAM. Particle filter SLAM.
| + | |
| − | |Anki Cozmo mobile robot
| + | |
| − | |Anki Cozmo SDK, OpenCV
| + | |
| − | |Python tutorial. Cozmo SDK API. OpenCV Python tutorial.
| + | |
| − | |Cozmo object recognition
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| − | |- style="vertical-align: top;"
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| − | | 5
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| − | | 10
| + | |
| − | |Mobile robots IV
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| − | |Graph search path planning. Potential field path planning. Navigation. Obstacle avoidance. Object search.
| + | |
| − | |Anki Cozmo mobile robot
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| − | |Anki Cozmo SDK, OpenCV
| + | |
| − | |Python tutorial. Cozmo SDK API. OpenCV Python tutorial.
| + | |
| − | |Cozmo navigation
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| − | |- style="vertical-align: top;"
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| − | | 6
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| − | | 11
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| − | |Robot arms I
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| − | |Homogeneous transformations. Frame-based pose specification. Denavit-Hartenberg specifications. Robot kinematics.
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| − | |Lynxmotion 5DoF arm, Arduino interface
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| − | |Arduino sketch programs for Lynxmotion
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| − | |Paul (1981), Chapters 1 & 2.
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| − | |Move end-effector along various paths in joint space
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| − | |- style="vertical-align: top;"
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| − | | 6
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| − | | 12
| + | |
| − | |Robot arms II
| + | |
| − | |Analytic inverse kinematics. Iterative approaches. Kinematic structure learning. Kinematics structure correspondences.
| + | |
| − | |Lynxmotion 5DoF arm, Arduino interface
| + | |
| − | |Arduino sketch programs for Lynxmotion
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| − | |Paul (1981), Chapter 3.
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| − | |Move end-effector along various paths in Cartesian frame of reference
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| − | |- style="vertical-align: top;"
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| − | | 7
| + | |
| − | | 13
| + | |
| − | |Robot arms III
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| − | |Robot manipulation. Frame-based task specification. Vision-based pose estimation.
| + | |
| − | |Lynxmotion 5DoF arm, Arduino interface
| + | |
| − | |Arduino sketch programs for Lynxmotion
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| − | |Vernon (1991), Sections 8.1-8.4.
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| − | |Compute the pose of a light cube
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| − | |- style="vertical-align: top;"
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| − | | 7
| + | |
| − | | 14
| + | |
| − | |Robot arms IV
| + | |
| − | |Language-based programming. Programming by demonstration.
| + | |
| − | |Lynxmotion 5DoF arm, Arduino interface
| + | |
| − | |Arduino sketch programs for Lynxmotion
| + | |
| − | |Vernon (1991), Sections 8.1-8.4
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| − | |Implement a program to move light cube from one position/pose to another position/pose
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| − | |- style="vertical-align: top;"
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| − | | 8
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| − | | 15
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| − | |- style="vertical-align: top;"
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| − | | 8
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| − | | 16
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| − | |
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| − | |- style="vertical-align: top;"
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| − | | 9
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| − | | 17
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| − | |Cognitive architectures I
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| − | |Role and requirements; cognitive architecture schemas; example cognitive architectures including Soar, ACT-R, Clarion, LIDA, and ISAC. The Standard Model.
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| − | |
| + | |
| − | |
| + | |
| − | |Vernon (2014) Chapter 3. Chella et al. (2013). Scheutz et al. (2013). Vernon et al. (2016).
| + | |
| − | |Group discussion on which cognitive architectures are suitable for cognitive robotics
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| − | |- style="vertical-align: top;"
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| − | | 9
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| − | | 18
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| − | |Cognitive architectures II
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| − | |CRAM: Cognitive Robot Abstract Machine. CRAM Plan Language (CPL). KnowRob knowledge processing and reasoning
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| − | |
| + | |
| − | |CRAM
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| − | |Beetz et al. (2010)
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| − | |CRAM test programs
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| − | |- style="vertical-align: top;"
| + | |
| − | | 10
| + | |
| − | | 19
| + | |
| − | |Learning and development I
| + | |
| − | |Supervised, unsupervised, and reinforcement learning. Hebbian learning.
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| − | |
| + | |
| − | |MaxHebb library
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| − | |Harmon and Harmon (1997)
| + | |
| − | |Hebbian learning
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| − | |- style="vertical-align: top;"
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| − | | 10
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| − | | 20
| + | |
| − | |Learning and development II
| + | |
| − | |Predictive sequence learning (PSL).
| + | |
| − | |Anki Cozmo mobile robot
| + | |
| − | |Anki Cozmo SDK, PSL library
| + | |
| − | |Sun and Giles (2001). Billing et al. (2011, 2016).
| + | |
| − | |PSL test programs
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| − | |- style="vertical-align: top;"
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| − | | 11
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| − | | 21
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| − | |Learning and development III
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| − | |Cognitive development in humans and robots.
| + | |
| − | |Anki Cozmo mobile robot
| + | |
| − | |Anki Cozmo SDK
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| − | |Vernon (2014), Chapters 6 & 9. |Lungarella et al. (2003). Asada et al. (2009). Cangelosi and Schlesinger (2015), Chapters 1 & 2.
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| − | |PSL test programs
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| − | |- style="vertical-align: top;"
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| − | | 11
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| − | | 22
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| − | |Learning and development IV
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| − | |Value systems for developmental and cognitive robots.
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| − | |Merrick (2016). Vernon et al. (2016).
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| − | |Group discussion on cognitive development in robotics
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| − | |- style="vertical-align: top;"
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| − | | 12
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| − | | 23
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| − | |Memory and Prospection
| + | |
| − | |Declarative vs. procedural memory. Semantic memory. Episodic memory
| + | |
| − | |Anki Cozmo mobile robot
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| − | |Anki Cozmo SDK, CINDY library, OpenCV
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| − | |Vernon (2014), Chapter 7.
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| − | |Implementation of episodic memory on Cozmo
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| − | |- style="vertical-align: top;"
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| − | | 12
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| − | | 24
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| − | |Internal simulation I
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| − | |Forward and inverse models, internal simulation hypothesis, internal simulation with PSL
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| − | |Anki Cozmo mobile robot
| + | |
| − | |Anki Cozmo SDK, PSL library
| + | |
| − | |Vernon (2014), Chapter 8. Billing et al. (2016).
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| − | |Exercises on PSL test programs
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| − | |- style="vertical-align: top;"
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| − | | 13
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| − | | 25
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| − | |Internal simulation II
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| − | |HAMMER cognitive architecture
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| − | |Boost, Imperial College London HAMMER library
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| − | |Demiris and Khadhouri (2006). Sarabia et al. (2011).
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| − | |Exercise on HAMMER tutorial using the ICL library
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| − | |- style="vertical-align: top;"
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| − | | 13
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| − | | 26
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| − | |Visual attention
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| − | |Visual attention. Spatial attention vs. selective attention. Saliency functions. Selective Tuning. Overt attention. Inhibition of return. Habituation. Top-down attention.
| + | |
| − | |Anki Cozmo mobile robot
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| − | |Anki Cozmo SDK, CINDY library
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| − | |Borji and Itti (2013).
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| − | |Implement visual attention on Cozmo
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| − | |- style="vertical-align: top;"
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| − | | 14
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| − | | 27
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| − | |Social interaction I
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| − | |Joint action. Joint attention. Shared intention. Shared goals. Perspective taking. Theory of mind.
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| − | |Orabec Astra RGB-D sensor
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| − | |Ubuntu 14.04, ROS, Imperial College London Perspective Taking library
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| − | |Vernon (2014), Chapter 9.
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| − | |Exercise on perspective taking using the ICL library
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| − | |- style="vertical-align: top;"
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| − | | 14
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| − | | 28
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| − | |Social interaction II
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| − | |Action and intention recognition. Learning from demonstration. Humanoid robotics.
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| − | |Kinect RGB-D sensor
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| − | |PSL library
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| − | |Billard et al. (2008). Argall (2009).
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| − | |Exercise on learning from demonstration using the PSL library
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| − | |}
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| − | </small>
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| − | | + | |
| − | | + | |
| − | ----
| + | |
| − | Back to [[Cognitive Robotics]]
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