![](bg2.png)
itives to broaden the locomotor behaviors has proven
complex, as well as the design of feedback mecha-
nisms for the adaptation and correction of locomo-
tion. Some authors tackle this problem through im-
itation and learning from demonstration (Nakanishi
et al., 2004), optimization of parameterized trajecto-
ries (Kim et al., 2009) or reinforcementlearning (Sug-
imoto and Morimoto, 2011). In this work, we take a
distinct approach, where the goal is to apply Genetic
Programming to the automatic exploration of: 1) the
motion primitives within the CPG, and 2) the integra-
tion of sensory inputs into feedback mechanisms for
the adaptability to the environment.
Evolutionary Computation (EC) algorithms rely
on the concept of Darwin’s evolution theory to find
optimized solutions for a target problem, such as
Genetic Algorithms (GA) and Genetic Programming
(GP). The former considers a control policy whose
configuration is evolved as a string of chromosomes
- configuration parameters for a target problem. The
latter evolves a complete control program for the task
at hand. These methods use a fitness function that
evaluates the candidate solutions, or individuals, and
whose value is used as quality measure for a set of
evolutionary operators (selection, crossover and mu-
tation).
Candidate solutions in GP, or individuals, can
fully describe the solution to the target problem, not
requiring any a-priori structure. Therefore, although
the complexity of the search space is increased, it is
expected to generate more adequate solutions to a par-
ticular problem.
GP has proven to be useful in the generation of
locomotion for very different types of robotic plat-
forms, thus showing its efficiency in finding solutions
for problems with a high levelof complexity. In (Gritz
and Hahn, 1997) a generic controller for an animated
physically plausible 3D character was created: an ar-
ticulated lamp. In (Tanev et al., 2005) a locomotion
controller for an articulated, snake like robot was cre-
ated. GP was also employed to generate a legged lo-
comotion controller for a quadruped robot in (Ander-
sson et al., 2000).
This method has also been applied in the genera-
tion of biped locomotion. In (Ok et al., 2001), GP was
applied in the automatic generation of feedback neu-
ral networks for the control of a simulated 3D biped
model with 32 muscles that controlled rigid segments
of the legs, body and arms. The model was able to
generate locomotion during only four steps. In (Ok
and Kim, 2005), these results were improved by ap-
plying an enhanced adaptive mutation operator that
reduced the search space and improved the evolution
results, increasing the generated steps to 10. Although
this work yielded interesting results, it is applied on a
very specific model, which physical and mechanical
properties do not fit common biped robotic platforms.
Other works address the generation of controllers
to robotic platforms through the use of GP. In (Wolff
and Wahde, 2007), Linear Genetic Programming
(LGP) was used to generate a locomotion controller
with feedback pathways, for a robust and anthropo-
morphic biped robot model. The model is simulated
butphysically plausible. There was no a-priori knowl-
edge about the mechanical or physical properties of
the body. Instead, the evolution uses feedback from
several sensor modalities (e.g. joints and several ac-
celerometers in the body and in the legs) to success-
fully achieve biped locomotion.
The work proposed in (Wolff and Nordin, 2003)
presents the generation of robot legged locomotion in
flat ground using LGP. A primary solution generated
in simulation would be passed on to a physical robot.
However, the achieved solution could not be executed
in the physical platform.
We intend to use GP to automatically search the
solution landscape and find solutions that rely on a
set of motion primitives. We also explore the use of
feedback pathways as a means to enable adaptation
to the environment features, particularly to adapt the
locomotion to walk up and down slopes in the en-
vironment. We are particularly interested in the im-
pact of sensory inclusion in the robot behavior herein
assessed considering Center of Mass (CoM) trajec-
tory. This provides for an understanding of how
feedback enhanced the locomotion skills of a biped
robot. Results demonstrate the smooth locomotion
achieved by the proposed GP mechanism and the
added adaptability to the environment, provided by
the inclusion of feedback pathways directly onto the
controller. Therefore, movement is generated in en-
trainment with the environment.
The paper is organized as follows. The following
section presents the locomotion model used to con-
trol the target platform. Then in section 3 the GP evo-
lution mechanism is presented, where the individu-
als for the current evolution process and the evolution
configuration are defined. Lastly, the results are pre-
sented in section 4, followed by a discussion in sec-
tion 5 and conclusions and future work in section 6.
2 BIPED LOCOMOTION MODEL
The basis of the locomotion controller used in this
work was previously presented in (Matos and Santos,
2012), where we proposed a Central Pattern Gener-
ator (CPG) integrated with local sensory feedback,
ICINCO2014-11thInternationalConferenceonInformaticsinControl,AutomationandRobotics
54