8,980 research outputs found
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
Kick control: using the attracting states arising within the sensorimotor loop of self-organized robots as motor primitives
Self-organized robots may develop attracting states within the sensorimotor
loop, that is within the phase space of neural activity, body, and
environmental variables. Fixpoints, limit cycles, and chaotic attractors
correspond in this setting to a non-moving robot, to directed, and to irregular
locomotion respectively. Short higher-order control commands may hence be used
to kick the system from one self-organized attractor robustly into the basin of
attraction of a different attractor, a concept termed here as kick control. The
individual sensorimotor states serve in this context as highly compliant motor
primitives.
We study different implementations of kick control for the case of simulated
and real-world wheeled robots, for which the dynamics of the distinct wheels is
generated independently by local feedback loops. The feedback loops are
mediated by rate-encoding neurons disposing exclusively of propriosensoric
inputs in terms of projections of the actual rotational angle of the wheel. The
changes of the neural activity are then transmitted into a rotational motion by
a simulated transmission rod akin to the transmission rods used for steam
locomotives.
We find that the self-organized attractor landscape may be morphed both by
higher-level control signals, in the spirit of kick control, and by interacting
with the environment. Bumping against a wall destroys the limit cycle
corresponding to forward motion, with the consequence that the dynamical
variables are then attracted in phase space by the limit cycle corresponding to
backward moving. The robot, which does not dispose of any distance or contact
sensors, hence reverses direction autonomously.Comment: 17 pages, 9 figure
When the goal is to generate a series of activities: A self-organized simulated robot arm
Behavior is characterized by sequences of goal-oriented conducts, such as
food uptake, socializing and resting. Classically, one would define for each
task a corresponding satisfaction level, with the agent engaging, at a given
time, in the activity having the lowest satisfaction level. Alternatively, one
may consider that the agent follows the overarching objective to generate
sequences of distinct activities. To achieve a balanced distribution of
activities would then be the primary goal, and not to master a specific task.
In this setting, the agent would show two types of behaviors, task-oriented,
and task-searching phases, with the latter interseeding the former.
We study the emergence of autonomous task switching for the case of a
simulated robot arm. Grasping one of several moving objects corresponds in this
setting to a specific activity. Overall, the arm should follow a given object
temporarily and then move away, in order to search for a new target and
reengage. We show that this behavior can be generated robustly when modeling
the arm as an adaptive dynamical system. The dissipation function is in this
approach time dependent. The arm is in a dissipative state when searching for a
nearby object, dissipating energy on approach. Once close, the dissipation
function starts to increase, with the eventual sign change implying that the
arm will take up energy and wander off. The resulting explorative state ends
when the dissipation function becomes again negative and the arm selects a new
target. We believe that our approach may be generalized to generate
self-organized sequences of activities in general.Comment: 10 pages, 7 figure
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.Comment: GECCO '18: Genetic and Evolutionary Computation Conference, 2018,
Kyoto, Japa
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It enables the discovery and
acquisition of large repertoires of skills through self-generation,
self-selection, self-ordering and self-experimentation of learning goals. We
present an algorithmic approach called Intrinsically Motivated Goal Exploration
Processes (IMGEP) to enable similar properties of autonomous or self-supervised
learning in machines. The IMGEP algorithmic architecture relies on several
principles: 1) self-generation of goals, generalized as fitness functions; 2)
selection of goals based on intrinsic rewards; 3) exploration with incremental
goal-parameterized policy search and exploitation of the gathered data with a
batch learning algorithm; 4) systematic reuse of information acquired when
targeting a goal for improving towards other goals. We present a particularly
efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a
population-based policy and an object-centered modularity in goals and
mutations. We provide several implementations of this architecture and
demonstrate their ability to automatically generate a learning curriculum
within several experimental setups including a real humanoid robot that can
explore multiple spaces of goals with several hundred continuous dimensions.
While no particular target goal is provided to the system, this curriculum
allows the discovery of skills that act as stepping stone for learning more
complex skills, e.g. nested tool use. We show that learning diverse spaces of
goals with intrinsic motivations is more efficient for learning complex skills
than only trying to directly learn these complex skills
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