Demonstrations |
| 2012-04-30 | SBL Visual Intelligence |
| Application of SBL to verb recognition in video data streams. The robot is the observer and the actions are carried out by humans. |  |
| Author(s) | NHK |
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| 2011-08-08 | SBL on Gatchan NHK |
| Demo/Intro of SBL on SuperBot for the Japanese NHK Gatchan show. |  |
| Author(s) | NHK |
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| 2011-03-28 | Adapting to unpredicted changes |
| The robot explores the office through random executions of the left and right actions. A goal is reached by planning with the learned model. A simultaneous unpredicted sensor, action, environment and goal change is introduced by rotating the camera, toggling the left and right actions and moving a goal book. SBL detects surprises, repairs its model and tracks the goal. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2010-09-01 | Learning to Turn Left/Right in an Office |
| SBL learns the result of the turn left and turn right actions in the office. It it tasked to match the goal scene using only these actions after learning them. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2010-08-24 | Learning Forward/Backward in an Office |
| SBL learns the result of the forward and backward actions in the office. It it tasked to match the goal scene using only these actions after learning them. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2010-08-22 | Real-World Office Environment |
| The real-world office environment used for testing autonomous navigation. This consists of a SuperBot module being controlled by a laptop via Bluetooth in a typical office room with a few magazines/books placed scattered around. Objects are detected using SURF features. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2009-11-15 | Unpredicted sensor change |
| Demonstration of learning before and after an unpredicted camera sensor change. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-08-29 | Fault Tolerance Experiment 5 |
| The improved Surprise-Based algorithm learning with autonomous sensor & actuator coupling, rule forgetting and feature relevance. The left turn and right turn actions are toggled after a model has been learned so as to simulate actuator failure. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-08-27 | Fault Tolerance Experiment 4 |
| The improved Surprise-Based algorithm learning with autonomous sensor & actuator coupling, rule forgetting and feature relevance. The camera is flipped by 180 degrees to simulate sensor failure. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-08-26 | Fault Tolerance Experiment 3 |
| The improved Surprise-Based algorithm learning with autonomous sensor & actuator coupling, rule forgetting and feature relevance. A random valued sensor with no correlation to the environment is added to test feature relevance |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-08-25 | Fault Tolerance Experiment 2 |
| The improved Surprise-Based algorithm learning with autonomous sensor & actuator coupling, rule forgetting and feature relevance. A constant valued sensor is added to test feature relevance |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-07 | SBL experimental setup |
| A flyby of the surprise-based learning experimental environment and robot |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-07 | SBL sensor toggle experiment - external |
| An external camera view of the an SBL experiment where the camera is flipped 180 degrees after learning for a while. Targetting after learning for a short while is shown, followed by targetting after sensor toggle and relearning |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-07 | SBL sensor toggle experiment - data |
| An internal data view of an SBL experiment where the camera is flipped 180 degrees after learning for a while. Targetting after learning for a short while is shown, followed by targetting after sensor toggle and relearning |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion prior to learning - line |
| The target is the red wall and white floor seen from a particular distance. The robot has not learnt how to move backwards to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion prior to learning - turn |
| The target is the green wall. The robot has not learnt how to turn to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion prior to learning - corner |
| The target is the corner of the red and green walls. The robot has not learnt how to turn to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion after learning - line |
| The target is the red wall and white floor seen from a particular distance. The robot has learnt how to move backwards to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion after learning - turn |
| The target is the red wall. The robot has learnt how to turn to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion after learning - corner |
| The target is the corner of the red and yellow walls. The robot has learnt how to turn to accomplish this. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera - line |
| The camera is flipped while the robot was learning. The robot is unable to move to target due to surprises |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera - turn |
| The camera is flipped while the robot was learning. The robot is unable to turn towards the green wall without further learning |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera - corner |
| The camera is flipped while the robot was learning. The robot is unable to turn towards the corner of the blue and red walls |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera and relearning - line |
| The camera is flipped while the robot was learning and is allowed to learn for a while longer. The robot is able to learn move backwards to see the green wall and white floor at a distance. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera and relearning - turn |
| The camera is flipped while the robot was learning and is allowed to learn for a while longer. The robot adapts to turn towards the blue wall. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-06-06 | SBL motion with flipped camera and relearning - corner |
| The camera is flipped while the robot was learning and is allowed to learn for a while longer. The robot adapts to turn towards the corner of the green and yellow walls. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-05-12 | SBL Complementary Rules & Multi Stage Planner |
| The complementary rules have been padded to accomodate ABSENT explicitly. Also the planner has been modified to find a route to the targets in the first stage, move to remove fiducials that are not a part of the target scene and finally plan to adjust the sizes |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-04-10 | SBL sensor toggle |
| The camera image has been flipped along the vertical axis. SBL is still able to learn and track a target. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-04-09 | SBL tracking target |
| Learning a world model and using it to track a target which is set during runtime. |  |
| Author(s) | Nadeesha Ranasinghe |
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| 2008-04-07 | SBL world model |
| This video shows how SBL creates a compact world model with very few surprises. |  |
| Author(s) | Nadeesha Ranasinghe |
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