6,812 research outputs found
Scalable Co-Optimization of Morphology and Control in Embodied Machines
Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Exploring the Modularity and Structure of Robots Evolved in Multiple Environments
Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolu- tionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments.
This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot’s morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost.
I utilize a tree morphology which makes my results generalizable to other mor- phologies while also allowing in depth theoretical analysis about the properties rel- evant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which mea- sures how much independence a robot exhibits with regards to environmental stimu- lus.
My work extends beyond evolutionary robotics and can be applied to the opti- mization of embodied systems in general as well as provides insight into the evolution of form in biological organisms
The agent architecture InteRRaP : concept and application
One of the basic questions of research in Distributed Artificial Intelligence (DAI) is how agents have to be structured and organized, and what functionalities they need in order to be able to act and to interact in a dynamic environment. To cope with this question is the purpose of models and architectures for autonomous and intelligent agents. In the first part of this report, InteRRaP, an agent architecture for multi-agent systems is presented. The basic idea is to combine the use of patterns of behaviour with planning facilities in order to be able to exploit the advantages both of the reactive, behaviour-based and of the deliberate, plan-based paradigm. Patterns of behaviour allow an agent to react flexibly to changes in its environment. What is considered necessary for the performance of more sophisticated tasks is the ability of devising plans deliberately. A further important feature of the model is that it explicitly represents knowledge and strategies for cooperation. This makes it suitable for describing high-level interaction among autonomous agents. In the second part of the report, the loading-dock domain is presented, which has been the first application the InteRRaP agent model has been tested with. An automated loading-dock is described where the agent society consists of forklifts which have to load and unload trucks in a shared, dynamic environment
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