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Update other-aspects.md #101

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4 changes: 2 additions & 2 deletions docs/overview/architecture-overview/other-aspects.md
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Expand Up @@ -30,12 +30,12 @@ Hierarchy can also be leveraged for goal-states, where a more abstract goal-stat

# Sub-Cortical Motor Areas

The architecture is an entire sensorimotor system. Each learning module receives sensory input and an efference copy of the motor command and outputs a feature-at-pose along with a motor command. Since many modules may produce conflicting motor commands (e.g. different patches on the retina cannot move in opposite directions) they usually need to be coordinated in a motor area. This motor area contains an action policy that decides which action commands to execute in the world based on the motor outputs from all learning modules. It also needs to translate the goal state outputs of the learning modules into motor commands for the actuators. It then sends this motor command to the actuators of the body and an efference copy of it back to the sensor modules.
The architecture is an entire sensorimotor system. Each learning module receives sensory input and an efference copy of the motor command and outputs a feature-at-pose along with a motor command. Since many modules may produce conflicting motor commands (e.g., different patches on the retina cannot move in opposite directions) they usually need to be coordinated in a motor area. This motor area contains an action policy that decides which action commands to execute in the world based on the motor outputs from all learning modules. It also needs to translate the goal state outputs of the learning modules into motor commands for the actuators. It then sends this motor command to the actuators of the body and an efference copy of it back to the sensor modules.

In the brain, a lot of this processing occurs subcortically. Therefore in our system, we also don't need to resolve these issues within a learning module but can do it within a separate motor area. However, we need to keep in mind that the motor area does not know about the models of objects that are learned in the learning modules and therefore needs to receive useful model-based motor commands from the LMs.

# Predictions

Learned models in the memory of the learning module can be used to make predictions about future observations. If there are multiple models that match the current observations, the predictions would have more uncertainty attached to them. The prediction error can be used as a learning signal to update models or as a criterion for matching during object recognition.

Currently there is no prediction in time, although in the future such capabilities will be added via the inclusion of a temporal dimension. This will help support encoding behaviors of objects, as well as predictions that can be used for motor-policy planning. For example, the long-term aim is for the architecture to be able to predict how a simple object such as a stapler evolves as it is opened or closed, or to coarsely model the physical properties of common materials.
Currently there is no prediction in time, although in the future such capabilities will be added via the inclusion of a temporal dimension. This will help support encoding behaviors of objects, as well as predictions that can be used for motor-policy planning. For example, the long-term aim is for the architecture to be able to predict how a simple object such as a stapler evolves as it is opened or closed, or to coarsely model the physical properties of common materials.
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