Properties of robot
Must have all of the following:
- Autonomous: based on own decisions, not teleoperated, being able to maintain itself
- exist: being able to act in the world
- sense: being able to perceive the world
- act on senses: adapt
- achieve goals: used sense d information to a goal
What is the frame-of-reference problem?
Simple underlying mechanism, but complex behaviour. ! consequence is tend to overestimate complexity of mechanisms
What is behaviour?
“brain” + agent environment interaction
What is control theory?
the mathematical study of the properties of automated control systems
Cybernetics
studying biological systems from the level of neurons, to the level of behaviour, and trying to implement similar principles in simple robots using methods from control theory.
Artificial life
focuses on computational, not physical, systems that exist within a computer, not in the real world
excitatory connections
stimulating
inhibitory connections
weakening
What do we need to keep in mind with embodiment?
- obey rules of physics (takes time, cant be at two places etc)
- needs to be aware of itself and other bodies/objects
- limitations to movements and what it can sense
- Limit on how fast the robot can move –> react
Learning
acquiring new knowledge or skills and improve one’s performance
A robot can learn about(3):
- itself (internal)
- its environment (maps etc)
- other robots and people
Benefits of learning (in robotics, 3)
- Perform task better –> no controller is perfect
- Adapting to changes
- Simplify programming work
Disadvantages of supervised learning(3)
- a trainer is needed –> less autonomous
- not learning when operating
- first training then operating phase
What is the training info with Supervised learning?
The input training info is: the wanted output for each input
What is the input for unsupervised learning and for what is it useful?
Input: only inputs
Useful to preprocess inputs: cluster data in meaningful clusters
What is the training info for reinforcement learning?
The input is: Evaluations (rewards/penalties)
How does a reinforcement learning system learns then?
- Trial and error search –> adjusts weights
Exploration
Try each state to see which is optimal.
What is exploitation?
Use what a robot already has learned
What is a control policy?
A complete state action pair table (most often created by reinforcement learning)
What is temporal credit assignment?
when in reinforcement learning after a long time the feedback, the error has to be back-propagated
Define biomemetic
machines with properties similar to those of biological systems; imitate biological systems ion some way
What is a synthetic approach?
Easy rules in the brain can creatie complex behaviour. It is easier to build and then increase complexity than understand what is precisely going on based on it’s behaviors.
What is spatial credit assignment?
assigning credit or blame to actions taken by members of a team and not the agent itself
