1. Problems with planning
  2. An alternative view: behavioral robotics
  3. Emergence
  4. Back to Brooks and his behavioral approach
  5. How behavior-based robotics works
  6. Examples of robots with behavior-based architechtures
  7. Making Genghis walk
  8. More complex behaviors
  9. Making Genghis walk with a classical planning architecture

Problems with planning

NB: We say that robot architectures that rely on planning follow the classical view. i.e.,they are based on representation and manipulation of explicit knowledge.

An alternative view: behavioral robotics

Questioning of the classical view began in the mid 1980s. We'll look at just one approach that was proposed: Behavioral robotics (Rodney Brooks).

Inspiration for behavioral robotics includes the following:

The theory behind the work of Rodney Brooks et al: The world is its own best model. It is always exactly up to date. It always contains every detail there is to be known. The trick is to sense it appropriately and often enough.


Notion that complex phenomena arise from the (serendipitously) coordinated activity of simpler phenomena. Some examples from nature include: Philosopher John Searle has argued that consciousness is an emergent property of the brain.

We all know (I hope) that bats use echolocation to detect prey. The prey have techniques of their own, however to elude the bats. [The following is taken from Barry Sinervo, 1997.]

Moths receive ultrasonic bat vocalizations with two ears on each side of the thorax. When pressure waves from the high energy bat vocalizations strike the ears and vibrate the membranes of the moth ears, two sensory receptors (A1 and A2) can fire depending on the energy of the sound. The sensory neurons trigger an action potential in the sensory interneurons which conduct the electrical impulses to the next synapse. The next neuron in the chain after the first synapse is triggered by neurotransmitters which are released and cross the synaptic junction and trigger a new action potential. The impulse can travel to the brain in this manner, or to ganglia in the thorax. Neurons in the ganglia or brain can integrate the information and send an action potential on to motor neurons that cause muscles to fire. The differential senstivity of the A1 and A2 sensory neurons leads to a stimulus filtering of the bat sounds that gives the moth two options:

The A1 cell is sensitive to low energy sounds (e.g., distant bat calls), and the A2 is sensitive to high energy sounds (e.g., close bat calls).

We will focus on A2:

If the moth's long- to medium-distance evasive maneuvers fail, and the bat is about to collide, the A2 neuron begins firing because of the high energy reaching the moth ear. The A2 cells send a message to the thoracic ganglia, and this seems to shut down wing beats or cause them to fire erratically. This leads to erratic flight which may be a last ditch attempt to elude the ranging and speed computing neurons of the bat's brain.

Back to Brooks and his behavioral approach

Brooks argues that the number of years that evolution has spent on "higher level" function such as language, planning, intellectual enterprise, etc., is relatively tiny. The harder problems (behaviors) are the simple ones.

How behavior-based robotics works

Roboticists have been quite successful in demonstrating that many basic competences in the physical world can be achieved using simple, inexpensive mechanisms.

Examples of robots with behavior-based architectures

Making Genghis walk

Making Genghis walk on flat terrain involves only the following simple behaviors and a bit of coordination:
  1. Each leg moves in cycles:
  2. Coordinate so that 3 of the 6 legs (not all on the same side) are on the ground at any one time.
Walking on rugged terrain is obviously more complicated -- but not much more. Just a simple adjustment needs to be made. When a leg's forward motion is blocked, retract it, lift it higher, and try again:

The diagram above, which specifies simple action sequences, is called an AFSM (Augmented Finite State Machine). Timers control the amount of time it takes to traverse each arc in the AFSM.

Note that this is model-free. That is, it requires no model of the environment. It does not deliberate or use search for generating the controls.

More complex behaviors

The Subsumption Architecture offers the ability for synchronizing AFSMs. This enables the programmer to compose increasingly complex controllers.

Making Genghis walk with a classical planning architecture

Say we wanted to make Genghis walk by implementing a classical planning architecture that makes use of a configuration space representation: