13 research outputs found
Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations
For robots to handle the numerous factors that can affect them in the real
world, they must adapt to changes and unexpected events. Evolutionary robotics
tries to solve some of these issues by automatically optimizing a robot for a
specific environment. Most of the research in this field, however, uses
simplified representations of the robotic system in software simulations. The
large gap between performance in simulation and the real world makes it
challenging to transfer the resulting robots to the real world. In this paper,
we apply real world multi-objective evolutionary optimization to optimize both
control and morphology of a four-legged mammal-inspired robot. We change the
supply voltage of the system, reducing the available torque and speed of all
joints, and study how this affects both the fitness, as well as the morphology
and control of the solutions. In addition to demonstrating that this real-world
evolutionary scheme for morphology and control is indeed feasible with
relatively few evaluations, we show that evolution under the different hardware
limitations results in comparable performance for low and moderate speeds, and
that the search achieves this by adapting both the control and the morphology
of the robot.Comment: Accepted to the 2018 Genetic and Evolutionary Computation Conference
(GECCO
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
Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds
The complexity of a legged robot's environment or task can inform how
specialised its gait must be to ensure success. Evolving specialised robotic
gaits demands many evaluations - acceptable for computer simulations, but not
for physical robots. For some tasks, a more general gait, with lower
optimization costs, could be satisfactory. In this paper, we introduce a new
type of gait controller where complexity can be set by a single parameter,
using a dynamic genotype-phenotype mapping. Low controller complexity leads to
conservative gaits, while higher complexity allows more sophistication and high
performance for demanding tasks, at the cost of optimization effort. We
investigate the new controller on a virtual robot in simulations and do
preliminary testing on a real-world robot. We show that having variable
complexity allows us to adapt to different optimization budgets. With a high
evaluation budget in simulation, a complex controller performs best. Moreover,
real-world evolution with a limited evaluation budget indicates that a lower
gait complexity is preferable for a relatively simple environment.Comment: Accepted to EvoApplications1
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
Lamarckian Evolution of Simulated Modular Robots
We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies
Real-world evolution adapts robot morphology and control to hardware limitations
For robots to handle the numerous factors that can afect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a speciic environment. Most of the research in this ield, however, uses simpliied representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this afects both the itness, as well as the morphology and control of the solutions. In addition to demonstrating that this realworld evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the diferent hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.
© The Authors. Publication rights licensed to Association for Computing Machinery. This is the author's version. Not for redistribution. The definitive version was published in GECCO '18: Genetic and Evolutionary Computation Conference, https://doi.org/10.1145/3205455.320556