Intelligent Approaches in Locomotion - A Review <s> Fully connected recurrent NN <s> We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th... <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Fully connected recurrent NN <s> A fully connected recurrent ANN model is proposed as a generator of stable limit cycles. A hybrid genetic algorithm is used for training the network model. The behaviour of the model is presented through a number of simulation experiments and the stability of the generated limit cycles is tested under several conditions. > <s> BIB002 </s> Intelligent Approaches in Locomotion - A Review <s> Fully connected recurrent NN <s> In previous work, we demonstrated that genetic algorithms could be used to evolve dynamical neural networks for controlling the locomotion of a simulated hexapod agent. We also demonstrated that these evolved controllers were robust to loss of sensory feedback and other peripheral variations. In this paper, we show that these locomotion controllers, evolved in simulation, are capable of directing the walking of a real six-legged robot, and that many of the desirable properties observed in simulation carry over directly to the real world. In addition, we demonstrate that these controllers are amenable to hardware implementation and can thus be easily embodied within the robot. <s> BIB003 </s> Intelligent Approaches in Locomotion - A Review <s> Fully connected recurrent NN <s> We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion. <s> BIB004
In BIB004 , a network of ten fully connected leaky-integrator neurons was used as a pattern generator, where six of them were also connected to the actuators to output the trajectories. The output functions of the neurons insured changing outputs over time, even if the network was initialized with zero outputs for all neurons. Successful bipedal walking patterns were evolved using a GA, using distanced travelled in a set time frame as the fitness function. A similar network was used in BIB002 where it was evaluated for properties that may be useful in locomotion, by learning sinusoidal patterns. The parameters of the network were again evolved with a GA. The number of generations required was found to be largely independent of the NN sampling rate (how many iterations the network should perform per second). This implied that the network learned the form of the patterns rather than individual sample values and simply scaled output frequency to the sampling rate. The network was able to learn multiple sinusoidal patterns, selectable by an input level, but evolution took more generations as the number of patterns required increased. Learned patterns were resilient to noise at the inputs, and even if the input was disrupted by a large amount, the output would quickly return to normal when the input was corrected. It was found that a six neuron model was sufficient to store eight different sinusoidal patterns, as long as the form of those patterns was different. A six neuron fully connected RNN was also investigated in BIB001 , to control an agent moving to the location of a chemical marker in two dimensional space. The network was trained with GA but, although successful, a large population size was needed to avoid convergence to sub-optimal local solutions. A sparsely connected set of RNNs (one for each leg), consisting of five neurons each, was then applied to produce an insect gait. The approach was later shown to be successful in a real hexapod robot in BIB003 .