4 research outputs found
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
Chaotic exploration and learning of locomotor behaviours
Recent developments in the embodied approach to understanding the generation of
adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic
motor patterns should not be done in isolation from an appreciation, and indeed
exploitation, of neural-body-environment interactions. Utilising spontaneous mutual
entrainment between neural systems and physical bodies provides a useful passage
to the regions of phase space which are naturally structured by the neuralbody-
environmental interactions. A growing body of work has provided evidence
that chaotic dynamics can be useful in allowing embodied systems to spontaneously
explore potentially useful motor patterns. However, up until now there has
been no general integrated neural system that allows goal-directed, online, realtime
exploration and capture of motor patterns without recourse to external monitoring,
evaluation or training methods. For the first time, we introduce such a system
in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics,
for the exploration and learning of the possible locomotion patterns of an articulated
robot of an arbitrary morphology in an unknown environment. The controller
is modelled as a network of neural oscillators which are coupled only through physical
embodiment, and goal directed exploration of coordinated motor patterns is
achieved by a chaotic search using adaptive bifurcation. The phase space of the
indirectly coupled neural-body-environment system contains multiple transient or
permanent self-organised dynamics each of which is a candidate for a locomotion
behaviour. The adaptive bifurcation enables the system orbit to wander through
various phase-coordinated states using its intrinsic chaotic dynamics as a driving
force and stabilises the system on to one of the states matching the given goal
criteria. In order to improve the sustainability of useful transient patterns, sensory
homeostasis has been introduced which results in an increased diversity of motor outputs,
thus achieving multi-scale exploration. A rhythmic pattern discovered by this
process is memorised and sustained by changing the wiring between initially disconnected
oscillators using an adaptive synchronisation method. The dynamical nature
of the weak coupling through physical embodiment allows this adaptive weight learning
to be easily integrated, thus forming a continuous exploration-learning system.
Our result shows that the novel neuro-robotic system is able to create and learn a
number of emergent locomotion behaviours for a wide range of body configurations
and physical environment, and can re-adapt after sustaining damage. The implications
and analyses of these results for investigating the generality and limitations of
the proposed system are discussed
Robot arm control exploiting natural dynamics
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 143-150).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This thesis presents an approach to robot arm control exploiting natural dynamics. The approach consists of using a compliant arm whose joints are controlled with simple non-linear oscillators. The arm has special actuators which makes it robust to collisions and gives it a smooth compliant motion. The oscillators produce rhythmic commands of the joints of the arm, and feedback of the joint motions is used to modify the oscillator behavior. The oscillators enable the resonant properties of the arm to be exploited to perform a variety of rhythmic and discrete tasks. These tasks include tuning into the resonant frequencies of the arm itself, juggling, turning cranks, playing with a Slinky toy, sawing wood, throwing balls, hammering nails and drumming. For most of these tasks, the controllers at each joint are completely independent, being coupled by mechanical coupling through the physical arm of the robot. The thesis shows that this mechanical coupling allows the oscillators to automatically adjust their commands to be appropriate for the arm dynamics and the task. This coordination is robust to large changes in the oscillator parameters, and large changes in the dynamic properties of the arm. As well as providing a wealth of experimental data to support this approach, the thesis also provides a range of analysis tools, both approximate and exact. These can be used to understand and predict the behavior of current implementations, and design new ones. These analysis techniques improve the value of oscillator solutions. The results in the thesis suggest that the general approach of exploiting natural dynamics is a powerful method for obtaining coordinated dynamic behavior of robot arms.by Matthew M. Williamson.Ph.D