992 research outputs found
Modular Self-Reconfigurable Robot Systems
The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel
EMERGE Modular Robot: A Tool for Fast Deployment of Evolved Robots
This work presents a platform for evolution of morphology in full cycle reconfigurable hardware: The EMERGE (Easy Modular Embodied Robot Generator) modular robot platform. Three parts necessary to implement a full cycle process, i.e., assembling the modules in morphologies, testing the morphologies, disassembling modules and repeating, are described as a previous step to testing a fully autonomous system: the mechanical design of the EMERGE module, extensive tests of the modules by first assembling them manually, and automatic assembly and disassembly tests. EMERGE modules are designed to be easy and fast to build, one module is built in half an hour and is constructed from off-the-shelf and 3D printed parts. Thanks to magnetic connectors, modules are quickly attached and detached to assemble and reconfigure robot morphologies. To test the performance of real EMERGE modules, 30 different morphologies are evolved in simulation, transferred to reality, and tested 10 times. Manual assembly of these morphologies is aided by a visual guiding tool that uses AprilTag markers to check the real modules positions in the morphology against their simulated counterparts and provides a color feedback. Assembly time takes under 5Â min for robots with fewer than 10 modules and increases linearly with the number of modules in the morphology. Tests show that real EMERGE morphologies can reproduce the performance of their simulated counterparts, considering the reality gap. Results also show that magnetic connectors allow modules to disconnect in case of being subjected to high external torques that could damage them otherwise. Module tracking combined with their easy assembly and disassembly feature enable EMERGE modules to be also reconfigured using an external robotic manipulator. Experiments demonstrate that it is possible to attach and detach modules from a morphology, as well as release the module from the manipulator using a passive magnetic gripper. This shows that running a completely autonomous, evolution of morphology in full cycle reconfigurable hardware of different topologies for robots is possible and on the verge of being realized. We discuss EMERGE features and the trade-off between reusability and morphological variability among different approaches to physically implement evolved robots
The Effects of the Environment and Linear Actuators on Robot Morphologies
The field of evolutionary robotics uses principles of natural evolution to
design robots. In this paper, we study the effect of adding a new module
inspired by the skeletal muscle to the existing RoboGen framework: the linear
actuator. Additionally, we investigate how robots evolved in a plain
environment differ from robots evolved in a rough environment. We consider the
task of directed locomotion for comparing evolved robot morphologies. The
results show that the addition of the linear actuator does not have a
significant impact on the performance and morphologies of robots evolved in a
plain environment. However, we find significant differences in the morphologies
of robots evolved in a plain environment and robots evolved in a rough
environment. We find that more complex behavior and morphologies emerge when we
change the terrain of the environment
Exploring Robot Morphology Spaces through Breadth-First Search and Random Query
Evolutionary robotics offers a powerful framework for designing and evolving
robot morphologies, particularly in the context of modular robots. However, the
role of query mechanisms during the genotype-to-phenotype mapping process has
been largely overlooked. This research addresses this gap by conducting a
comparative analysis of query mechanisms in the brain-body co-evolution of
modular robots. Using two different query mechanisms, Breadth-First Search
(BFS) and Random Query, within the context of evolving robot morphologies using
CPPNs and robot controllers using tensors, and testing them in two evolutionary
frameworks, Lamarckian and Darwinian systems, this study investigates their
influence on evolutionary outcomes and performance. The findings demonstrate
the impact of the two query mechanisms on the evolution and performance of
modular robot bodies, including morphological intelligence, diversity, and
morphological traits. This study suggests that BFS is both more effective and
efficient in producing highly performing robots. It also reveals that
initially, robot diversity was higher with BFS compared to Random Query, but in
the Lamarckian system, it declines faster, converging to superior designs,
while in the Darwinian system, BFS led to higher end-process diversity.Comment: arXiv admin note: text overlap with arXiv:2303.12594. substantial
text overlap with arXiv:2309.1309
Acquiring moving skills in robots with evolvable morphologies: Recent results and outlook
© 2017 ACM. We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and controller architectures in combination with several learning algorithms, e.g. evolutionary algorithms, reinforcement learning, simulated annealing, and HyperNEAT. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development
Low-Rank Modular Reinforcement Learning via Muscle Synergy
Modular Reinforcement Learning (RL) decentralizes the control of multi-joint
robots by learning policies for each actuator. Previous work on modular RL has
proven its ability to control morphologically different agents with a shared
actuator policy. However, with the increase in the Degree of Freedom (DoF) of
robots, training a morphology-generalizable modular controller becomes
exponentially difficult. Motivated by the way the human central nervous system
controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR)
framework that exploits the redundant nature of DoF in robot control. Actuators
are grouped into synergies by an unsupervised learning method, and a synergy
action is learned to control multiple actuators in synchrony. In this way, we
achieve a low-rank control at the synergy level. We extensively evaluate our
method on a variety of robot morphologies, and the results show its superior
efficiency and generalizability, especially on robots with a large DoF like
Humanoids++ and UNIMALs.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022
A Comparative Study of Brain Reproduction Methods for Morphologically Evolving Robots
In the most extensive robot evolution systems, both the bodies and the brains
of the robots undergo evolution and the brains of 'infant' robots are also
optimized by a learning process immediately after 'birth'. This paper is
concerned with the brain evolution mechanism in such a system. In particular,
we compare four options obtained by combining asexual or sexual brain
reproduction with Darwinian or Lamarckian evolution mechanisms. We conduct
experiments in simulation with a system of evolvable modular robots on two
different tasks. The results show that sexual reproduction of the robots'
brains is preferable in the Darwinian framework, but the effect is the opposite
in the Lamarckian system (both using the same infant learning method). Our
experiments suggest that the overall best option is asexual reproduction
combined with the Lamarckian framework, as it obtains better robots in terms of
fitness than the other three. Considering the evolved morphologies, the
different brain reproduction methods do not lead to differences. This result
indicates that the morphology of the robot is mainly determined by the task and
the environment, not by the brain reproduction methods.Comment: 8 pages, ALif
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