A feedback control structure for on-line learning tasks

Huber, Manfred; Grupen, Roderic A.

doi:10.1016/s0921-8890(97)00044-4

Cited by 46 publications

(34 citation statements)

References 14 publications

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“…Whenever basic motion patterns are acquired through interaction with the environment, the possibility of critical failures increases. The issues of safety during gait learning using an actual robot are considered by Huber and Grupen (1997). One of their safety constraints requires that the walking robot should always remain statically stable.…”

Section: Related Workmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

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Section: Related Workmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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“…Access to such actions can make tasks easier to solve, if they effectively solve subtasks within the domain. There are several approaches to defining such temporally-extended actions: options (Sutton et al, 1999), skills (Konidaris and Barto, 2009;Konidaris et al, 2010), activities (Barto and Mahadevan, 2003), modes (Alur et al, 2000), and behaviors (Huber and Grupen, 1997;Brooks, 1986).…”

Section: Challengementioning

confidence: 99%

Evolving multimodal behavior through modular multiobjective neuroevolution

Schrum

2015

SIGEVOlution

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Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it would be advantageous for artificial agents, both in physical and virtual worlds, to exhibit a similar diversity of behaviors. Artificial evolution, in particular neuroevolution [3, 4], is known to be capable of discovering complex agent behavior. This dissertation expands on existing neuroevolution methods, specifically NEAT (Neuro-Evolution of Augmenting Topologies [7]), to make the discovery of multiple modes of behavior possible. More specifically, it proposes four extensions: (1) multiobjective evolution, (2) sensors that are split up according to context, (3) modular neural network structures, and (4) fitness-based shaping. All of these technical contributions are incorporated into the software framework of Modular Multiobjective NEAT (MM-NEAT), which can be downloaded here.

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“…These kinds of skills are not characterized by goal regions but rather by desired dynamic behavior, such as desired limit cycles. Little work has been done on creating options of this type, although much work in robotics is relevant, such as the methods used in the biped walker of Tedrake et al (2004) and the control-basis approach of Grupen and colleagues (Hart and Grupen 2011, 2012, Huber and Grupen 1997.…”

Section: Summary and Prospectsmentioning

confidence: 99%

Behavioral Hierarchy: Exploration and Representation

Barto

Konidaris

Vigorito

2013

Computational and Robotic Models of the Hierarchical Organization of Behavior

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Behavioral modules are units of behavior providing reusable building blocks that can be composed sequentially and hierarchically to generate extensive ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focusses on two features of behavioral hierarchy that appear to be less well recognized: its influence on exploratory behavior and the opportunity it affords to reduce the representational challenges of planning and learning in large, complex domains. Four computational examples are described that use methods of hierarchical reinforcement learning to illustrate the influence of behavioral hierarchy on exploration and representation. Beyond illustrating these features, the examples provide support for the central role of behavioral hierarchy in development and learning for both artificial and natural agents.

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A feedback control structure for on-line learning tasks

Cited by 46 publications

References 14 publications

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot

Evolving multimodal behavior through modular multiobjective neuroevolution

Behavioral Hierarchy: Exploration and Representation

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