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lirec_notes [2009-01-06 14:50] – davegriffiths | lirec_notes [2009-01-06 15:02] – davegriffiths | ||
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- | LIving with Robots and InteractivE Companions | + | ==== LIving with Robots and InteractivE Companions |
+ | |||
+ | LIREC aims to establish a multi-faceted theory of artificial long-term companions (including memory, emotions, cognition, communication, | ||
+ | |||
+ | Key areas of the research will focus on are: | ||
+ | |||
+ | ==Long term companions== | ||
+ | Robots and agents which build up a long term relationship with their users. | ||
+ | |||
+ | ==Agent migration== | ||
+ | In order to maintain a long term companionship, | ||
+ | * Mobile robot | ||
+ | * Fixed robot | ||
+ | * Handheld device | ||
+ | * Fixed graphical system | ||
+ | |||
+ | This migration between these devices, and how people relate to this migration is a core element of the research. | ||
===Scenarios=== | ===Scenarios=== | ||
+ | |||
+ | The scenarios are to be considered preliminary ideas for showcasing the technology developed for lirec, and highlight companionship and mostly exercise migration between devices. | ||
==Heriot Watt== | ==Heriot Watt== | ||
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between real and game world | between real and game world | ||
- | =University of Hertfordshire= | + | ==University of Hertfordshire== |
- | 1. Fetch and carry, help with physical impairment or convenience | + | |
- | 2. Cognitive prosthetic - memory aid for tasks etc | + | |
- | 3. Telepresence card player - robot mediates play | + | |
- | 4. Teaching Proxemic preferences - robot learns where to be relative to the user in different situations | + | |
- | 5. Travelling companion - agent migration, to stay with user during home, work, shopping. | + | |
===Architecture=== | ===Architecture=== | ||
Has to: | Has to: | ||
- | 1. Run on very different platforms | + | - Run on very different platforms |
- | 2. Reuse code across these platforms | + | |
- | 3. Support migration at runtime between platforms | + | |
Platforms can consist of 4 main types, mobile robot, fixed robot, handheld device or fixed graphical system. Each has it's inherent restrictions. | Platforms can consist of 4 main types, mobile robot, fixed robot, handheld device or fixed graphical system. Each has it's inherent restrictions. | ||
- | =Existing robot architectures= | + | ==Existing robot architectures== |
In the whole, there is a lack of sharing of this kind of technology. This is partly because generalising is hard in this field, considering all types of robots possible. However, Lirec has to generalise as it's using a wide variety of architectures. | In the whole, there is a lack of sharing of this kind of technology. This is partly because generalising is hard in this field, considering all types of robots possible. However, Lirec has to generalise as it's using a wide variety of architectures. | ||
NASREM/NIST RCS - NASA + ESA use a generic system with their subcontractors. | NASREM/NIST RCS - NASA + ESA use a generic system with their subcontractors. | ||
- | =Methodology= | + | ==Methodology== |
In the past there have been 2 broad approaches to robot design: | In the past there have been 2 broad approaches to robot design: | ||
- | * Heirarchical, | + | |
- | * Behavioural approach = less state, local decisions, liable to local minima, opaque to program | + | * Behavioural approach = less state, local decisions, liable to local minima, opaque to program |
This can be summed up as predictive vs reactive. | This can be summed up as predictive vs reactive. | ||
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The architecture will consist of 3 layers of abstraction: | The architecture will consist of 3 layers of abstraction: | ||
- | Level 1 - device layer, architecture dependant | + | * Level 1 - device layer, architecture dependant |
- | Level 2 - architecture dependant -> logical mappings | + | |
- | Level 3 - ION, device independant | + | |
Level 2 will provide a reference architecture with modular capabilities called competencies. Not all competencies will make sense for all architectures, | Level 2 will provide a reference architecture with modular capabilities called competencies. Not all competencies will make sense for all architectures, | ||
Example competencies: | Example competencies: | ||
- | * Face finding | + | |
- | * Expression recognition | + | * Expression recognition |
- | * Text to speech | + | * Text to speech |
- | * Obstacle avoidance | + | * Obstacle avoidance |
- | + | ||
YARP | YARP |