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lirec_notes [2009-01-06 14:50] – davegriffiths | lirec_notes [2009-01-06 15:08] – 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, | ||
+ | |||
+ | ==Agent platforms, and migration between them== | ||
+ | An interesting feature of agents which need to build up a long term relationship with their users will moving between different platforms. The platforms currently under investigation are: | ||
+ | * Mobile robots | ||
+ | * Fixed robots | ||
+ | * Handheld devices | ||
+ | * Fixed graphical systems | ||
+ | |||
+ | This migration between these devices, and how people relate to it, 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== | ||
Line 15: | Line 28: | ||
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, model based planning = expensive to maintain accurate world state model | + | |
- | * 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. | ||
- | Current thinking | + | The plan is to use a hybrid approach, example BIRON. Where the predictive constrains the reactive to combine local decisions with a world model. |
The architecture will consist of 3 layers of abstraction: | The architecture will consist of 3 layers of abstraction: | ||
- | Level 1 - device layer, | + | * Level 1 - device layer, |
- | Level 2 - architecture | + | |
- | 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, and different implementations of the same competency | + | Level 2 will provide a reference architecture with modular capabilities called competencies. Not all competencies will make sense for all platforms, and different implementations of the same competency |
Example competencies: | Example competencies: | ||
- | * Face finding | + | |
- | * Expression recognition | + | * Expression recognition |
- | * Text to speech | + | * Text to speech |
- | * Obstacle avoidance | + | * Obstacle avoidance |
- | + | ||
YARP | YARP |