Thesis Topic Details
Topic ID: |
44 | |
Title: |
Robot Learning | |
Supervisor: |
Claude Sammut | |
Research Area: |
Machine Learning, Artificial Intelligence, Robotics | |
| Associated Staff | ||
|---|---|---|
Assessor: |
Bernhard Hengst | |
| Topic Details | ||
Status: |
Active | |
Type: |
Research | |
Programs: |
CS CE BIOM BINF SE | |
Group Suitable: |
No | |
Industrial: |
No | |
Pre-requisites: |
COMP3411 and COMP9417 desirable but not essential. | |
Description: |
Most machine learning systems are one-shot meaning that they learn a single concept from data that are presented all at once. However, an intelligent agent like a robot must collect examples of concepts one by one in a complex environment that may be changing. The learning system must have properties that most common ones do not. It must be incremental , accumulating knowledge and building on already learnt concepts. It must be able to recover from mistakes and cope with a dynamic environment. It can also use active learning . That is, the robot can conduct experiments to test hypotheses. This project builds on previous work by students at UNSW. The aim is to equip one of our robots with some of the above capabilities. |
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Comments: |
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| Past Student Reports | ||
| No Reports Available. Contact the supervisor for more information.
Check out all available reports in the CSE Thesis Report Library. NOTE: only current CSE students can login to view and select reports to download. |
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