99 research outputs found
On Restricting Real-Valued Genotypes in Evolutionary Algorithms
Real-valued genotypes together with the variation operators, mutation and
crossover, constitute some of the fundamental building blocks of Evolutionary
Algorithms. Real-valued genotypes are utilized in a broad range of contexts,
from weights in Artificial Neural Networks to parameters in robot control
systems. Shared between most uses of real-valued genomes is the need for
limiting the range of individual parameters to allowable bounds. In this paper
we will illustrate the challenge of limiting the parameters of real-valued
genomes and analyse the most promising method to properly limit these values.
We utilize both empirical as well as benchmark examples to demonstrate the
utility of the proposed method and through a literature review show how the
insight of this paper could impact other research within the field. The
proposed method requires minimal intervention from Evolutionary Algorithm
practitioners and behaves well under repeated application of variation
operators, leading to better theoretical properties as well as significant
differences in well-known benchmarks
From real-time adaptation to social learning in robot ecosystems
While evolutionary robotics can create novel morphologies and controllers that are well-adapted to their environments, learning is still the most efficient way to adapt to changes that occur on shorter time scales. Learning proposals for evolving robots to date have focused on new individuals either learning a controller from scratch, or building on the experience of direct ancestors and/or robots with similar configurations. Here we propose and demonstrate a novel means for social learning of gait patterns, based on sensorimotor synchronization. Using movement patterns of other robots as input can drive nonlinear decentralized controllers such as CPGs into new limit cycles, hence encouraging diversity of movement patterns. Stable autonomous controllers can then be locked in, which we demonstrate using a quasi-Hebbian feedback scheme. We propose that in an ecosystem of robots evolving in a heterogeneous environment, such a scheme may allow for the emergence of generalist task-solvers from a population of specialists
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Lookup table partial reconfiguration for an evolvable hardware classifier system
Abstract—The evolvable hardware (EHW) paradigm relies on continuous run-time reconfiguration of hardware. When applied on modern FPGAs, the technically challenging reconfiguration process becomes an issue and can be approached at multiple levels. In related work, virtual reconfigurable circuits (VRC), partial reconfiguration, and lookup table (LUT) reconfiguration approaches have been investigated. In this paper, we show how fine-grained partial reconfiguration of 6-input LUTs of modern Xilinx FPGAs can lead to significantly more efficient resource utilization in an EHW application. Neither manual placement nor any proprietary bitstream manipulation is required in the simplest form of the employed method. We specify the goal archi-tecture in VHDL and read out the locations of the automatically placed LUTs for use in an online reconfiguration setting. This allows for an easy and flexible architecture specification, as well as possible implementation improvements over a hand-placed design. For demonstration, we rely on a hardware signal classifier application. Our results show that the proposed approach can fit a classification circuit 4 times larger than an equivalent VRC-based approach, and 6 times larger than a shift register-based approach, in a Xilinx Virtex-5 device. To verify the reconfiguration process, a MicroBlaze-based embedded system is implemented, and reconfiguration is carried out via the Xilinx Internal Configuration Access Port (ICAP) and driver software. I
Open-ended search for environments and adapted agents using MAP-Elites
Creatures in the real world constantly encounter new and diverse challenges
they have never seen before. They will often need to adapt to some of these
tasks and solve them in order to survive. This almost endless world of novel
challenges is not as common in virtual environments, where artificially
evolving agents often have a limited set of tasks to solve. An exception to
this is the field of open-endedness where the goal is to create unbounded
exploration of interesting artefacts. We want to move one step closer to
creating simulated environments similar to the diverse real world, where agents
can both find solvable tasks, and adapt to them. Through the use of MAP-Elites
we create a structured repertoire, a map, of terrains and virtual creatures
that locomote through them. By using novelty as a dimension in the grid, the
map can continuously develop to encourage exploration of new environments. The
agents must adapt to the environments found, but can also search for
environments within each cell of the grid to find the one that best fits their
set of skills. Our approach combines the structure of MAP-Elites, which can
allow the virtual creatures to use adjacent cells as stepping stones to solve
increasingly difficult environments, with open-ended innovation. This leads to
a search that is unbounded, but still has a clear structure. We find that while
handcrafted bounded dimensions for the map lead to quicker exploration of a
large set of environments, both the bounded and unbounded approach manage to
solve a diverse set of terrains
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment
Designing robots by hand can be costly and time consuming, especially if the
robots have to be created with novel materials, or be robust to internal or
external changes. In order to create robots automatically, without the need for
human intervention, it is necessary to optimise both the behaviour and the body
design of the robot. However, when co-optimising the morphology and controller
of a locomoting agent the morphology tends to converge prematurely, reaching a
local optimum. Approaches such as explicit protection of morphological
innovation have been used to reduce this problem, but it might also be possible
to increase exploration of morphologies using a more indirect approach. We
explore how changing the environment, where the agent locomotes, affects the
convergence of morphologies. The agents' morphologies and controllers are
co-optimised, while the environments the agents locomote in are evolved
open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the
diversity, fitness and robustness of agents evolving in environments generated
by POET to agents evolved in handcrafted curricula of environments. Our agents
each contain of a population of individuals being evolved with a genetic
algorithm. This population is called the agent-population. We show that
agent-populations evolving in open-endedly evolving environments exhibit larger
morphological diversity than agent-populations evolving in hand crafted
curricula of environments. POET proved capable of creating a curriculum of
environments which encouraged both diversity and quality in the populations.
This suggests that POET may be capable of reducing premature convergence in
co-optimisation of morphology and controllers.Comment: 17 pages, 8 figure
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Collaborative robots are becoming more common on factory floors as well as
regular environments, however, their safety still is not a fully solved issue.
Collision detection does not always perform as expected and collision avoidance
is still an active research area. Collision avoidance works well for fixed
robot-camera setups, however, if they are shifted around, Eye-to-Hand
calibration becomes invalid making it difficult to accurately run many of the
existing collision avoidance algorithms. We approach the problem by presenting
a stand-alone system capable of detecting the robot and estimating its
position, including individual joints, by using a simple 2D colour image as an
input, where no Eye-to-Hand calibration is needed. As an extension of previous
work, a two-stage transfer learning approach is used to re-train a
multi-objective convolutional neural network (CNN) to allow it to be used with
heterogeneous robot arms. Our method is capable of detecting the robot in
real-time and new robot types can be added by having significantly smaller
training datasets compared to the requirements of a fully trained network. We
present data collection approach, the structure of the multi-objective CNN, the
two-stage transfer learning training and test results by using real robots from
Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible
application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio
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