Behavior-Based Systems

Michaud, François; Nicolescu, Mircea

doi:10.1007/978-3-319-32552-1_13

Cited by 23 publications

(13 citation statements)

References 58 publications

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“…In contrast, behavior-based controls are more reactive in their traditional form [Michaud and Nicolescu, 2016]. They define a set of modules (called behaviors), which are composed of expected sensory input as the trigger and behavioral patterns that achieve a certain goal.…”

Section: Robot Control Architecturementioning

confidence: 99%

Improving HRI through robot architecture transparency

Hindemith¹,

Vollmer²,

Wiebel-Herboth³

et al. 2021

Preprint

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In recent years, an increased effort has been invested to improve the capabilities of robots. Nevertheless, human-robot interaction remains a complex field of application where errors occur frequently. The reasons for these errors can primarily be divided into two classes. Foremost, the recent increase in capabilities also widened possible sources of errors on the robot's side. This entails problems in the perception of the world, but also faulty behavior, based on errors in the system. Apart from that, non-expert users frequently have incorrect assumptions about the functionality and limitations of a robotic system. This leads to incompatibilities between the user's behavior and the functioning of the robot's system, causing problems on the robot's side and in the human-robot interaction. While engineers constantly improve the reliability of robots, the user's understanding about robots and their limitations have to be addressed as well. In this work, we investigate ways to improve the understanding about robots. For this, we employ FAMILIAR -FunctionAl user Mental model by Increased LegIbility ARchitecture, a transparent robot architecture with regard to the robot behavior and decision-making process. We conducted an online simulation user study to evaluate two complementary approaches to convey and increase the knowledge about this architecture to non-expert users: a dynamic visualization of the system's processes as well as a visual programming interface. The results of this study reveal that visual programming improves knowledge about the architecture. Furthermore, we show that with increased knowledge about the control architecture of the robot, users were significantly better in reaching the interaction goal. Furthermore, we showed that anthropomorphism may reduce interaction success.

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Section: Robot Control Architecturementioning

confidence: 99%

Improving HRI through robot architecture transparency

Hindemith¹,

Vollmer²,

Wiebel-Herboth³

et al. 2021

Preprint

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show abstract

“…The key area influenced by Brooks' ideas has been in robotics, where behaviour-based robotics is now an accepted component of robotic architecture [14,29,30,38,39]. One key example of the application of behaviour-based robotics is in NASA's Mars Exploration Rovers [40], robots that operate autonomously on the surface of Mars, using behaviour-based principles.…”

Section: Behaviour-based Robotics After Intelligence Without Represenmentioning

confidence: 99%

Intelligence without Representation: A Historical Perspective

Jordanous¹

2020

Systems

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This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without Representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by the evolutionary development of our own intelligence and that his approach mirrored how our own intelligence functions. Thus, the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact on artificial intelligence and cognitive theory at the time, as well as on modern-day approaches to AI.

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“…Behavior-based control approaches have been widely used to provide robots with capabilities as navigation, obstacle avoidance, mapping, wall following, and object manipulation, and they have been tested in applications involving different kinds of robots as mobile robots, underwater vehicles, manipulators, and humanoids. Seminal works on behavior-based control are reported in the papers (Brooks 1986) and (Arkin 1989); the book (Arkin 1998) represents one of the main reference textbook on the topic, while the Handbook's chapter (Michaud and Nicolescu 2016) provides a more up-to-date overview on the state of the art in the field.…”

Section: Examples Of Applicationmentioning

confidence: 99%

“…Different kinds of approaches for the motion control of autonomous robots have been developed in the past years; among the others, it is worth mentioning the following control strategies (see Michaud and Nicolescu 2016): • Deliberative control: based on the concept of "Think, then act," where the robot uses all the available information and knowledge to reason about what action to take next • Reactive control: based on the concept of "Don't Think, React," where a reactive system tightly couples perception to action, without intervening reasoning • Hybrid control: based on the concept of "Think and act concurrently," where the control system contains both the reactive component and the deliberative one, which must interact in order to produce a coherent output • Behavior-based control: based on the concept of "Think the way you act," where the control employs a set of distributed and interacting modules, named behaviors, that collectively achieve the desired system-level behavior Behavior-based control approaches, largely inspired from biological systems, have been developed with the aim of making a robot capable of operating in a real-word environment (often dynamic, unstructured, and unknown) without operating upon an abstract representation of the reality. The control approach is defined through a set of behaviors representing the reactions to different stimuli, e.g., motion directives to the robot actuators on the base of information provided by onboard sensing.…”

mentioning

confidence: 99%