Distributed neural control for complex autonomous robots

Chapter 9:Conclusions


This chapter discusses results obtained, points to possible uses of the architecture and describes future works.


Advantages of the DAIR method

Our approach is presented as a neural network-based modular architecture for controllers, trained by a neuro-evolutionary learning method, for robotic systems composed of multiple sensors and actuators. Our method has the following advantages:
1. By evolving modules separately, the search space dimension that the evolutionary algorithm has to afford is significatively reduced. This is what staged evolution attempts to accomplish.
2. At each new stage, the newly added modules will start evolving not from a random position in the search space, but from a place related to the new task to be evolved, which makes it easier to obtain the desired behaviour. This is what incremental evolution sets out to accomplish.

The DAIR method therefore combines both techniques (staged evolution and incremental evolution) into a single method, obtaining what we call as progressive design. The method is also general in terms of both robot and task.

Drawbacks for highly complex robots

1. When the number of modules is so large, it can be difficult to identify precisely which modules to use to start the evolutionary process, and which type of task to assign to them.

2. When the number of elements in the controller increases, the newly added modules in the last stage will have to interface with many previously evolved modules.

3. The use of a staged mechanism can lead to sub-optimal solutions.

Possible additional uses

This section discusses the use of the DAIR approach as paradigm for the solution of other related problems.

DAIR and scale-up in evolutionary robotics

One of the biggest problems that Evolutionary Robotics faces at present is that of scaling up, that is, the use of ER in complex robots. To avoid this pitfall, the DAIR architecture can be introduced as a solution.

Tactical modularity for resolution of general problems

Up until this point, the concepts of strategic and tactical modularity have only been applied to the control of robots. However, a step backwards can be taken to gain a wider perspective, and apply those concepts to more general problems where no devices exist, only abstract concepts or variables. Above all, it implies the use of DAIR for the optimization of functions, that is, to define the sub-goals required to generate a goal (strategic modules), and then to create tactical modules for the elements that appear in every sub-goal. We understand elements as the inputs required to generate the sub-goal, such as the sensors modules, and the outputs that define the sub-goal solution, like the actuator modules.

Tactical modularity and the robot inner world

The proposed IHU-based tactical modular architecture can be seen as a dynamical system approach to cognitive robotics using a controlled engineering perspective .Our claim is that the proposed network structure of IHU’s provides the autonomous agent with an inner world based on internal representations of perception rather than an explicit representational model, following the ideas of internal robotics in [Parisi, 2004] and the double closure scheme in [von Foerster, 1970]. In this architecture the concept of double closure is completely obtained, and sensors and actuators are completely coupled.

Figure 1: The ”Mind” designed through collaborative IHU’s in the form of a
MIMO and the decentralized control architecture

Future work

It follows a list of related future works.

Tactical modularity as a walking reflex system

Tactical modularity can be used to implement a completely coupled walking system between sensors and actuators, where the reflex system would be embedded into the walking mechanism. The walking system, mainly driven by the actuator signals, would have a directly coupled reflex system which is not a separated part of the walking, but rather an integrated part of it. It is suggested that animals have such type of a walking mechanism in order to improve their walking behaviour.

Deliberative control

It is possible to design a higher control layer which reads the current state of the reactive tactical structure, and then decides how to modify its behaviour through the use of a tonic signal. Deliberation of the higher layer would be based on the performance and current task required for the robot. This would include the capacity to deliberately control complex robots using only ANN’s in the DAIR architecture.

Figure 2: A deliberative structure using tactical modules.

Figure 3: Preliminary simulation created to implement a deliberative solution for
the Aibo robot in the T-Maze experiment (thanks to Francesc Espasa for actually  implementing it).

Liar IHU’s

To date, IHU modules have been desgined with a single output, which is used either for action in actuator-IHU’s, or as a processed sensor value in sensor-IHU’s. However, it could be interesting to investigate whether the architecture improves in both learning rate and fitness value if each IHU is allowed to have two or more outputs. The IHU would use one output for its related purpose, and the second one to communicate a, perhaps, different bit of information to the rest of IHU’s.

Web page by R. Téllez using rubric css by Hadley Wickham
Don't undertake a project unless it is manifestly important and nearly impossible (Edwin Land)