Inner rehearsal modeling for cognitive robotics

Braun, Jerome; Bergen, Karianne J.; Dasey, Timothy J.

doi:10.1117/12.888000

Cited by 3 publications

(4 citation statements)

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“…Humans can simulate the normal execution results of an action through inner rehearsal [40]. Taking advantage of the inner rehearsal mechanism, people can try to run the potential actions in their minds and predict their results, such as actions or decisions that are not explicitly executed [41,42].…”

Section: Inner Rehearsalmentioning

confidence: 99%

Robot Arm Reaching Based on Inner Rehearsal

Wang,

Zou,

Wei

et al. 2023

Biomimetics

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Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot’s ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively.

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Section: Inner Rehearsalmentioning

confidence: 99%

Robot Arm Reaching Based on Inner Rehearsal

Wang,

Zou,

Wei

et al. 2023

Biomimetics

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“…They range from conventional ones such as those based on optimal control (e.g., Kalman or particle filters) and probabilistic techniques, etc., to machine-intelligence techniques such as artificial neural networks, support vector machines (SVM), and other advanced machine-learning and -reasoning approaches. 1 Appropriateness of many of those approaches depends, to a large extent, on the task or application of the robotic or unmanned system.…”

Section: Introductionmentioning

confidence: 99%

“…1 In the subsequent two phases of our Cognitive Robotics effort we focused on the neurobiomimetic paradigm. This involves algorithmic emulation of neurobiological aspects, including brain regions and their interactions.…”

Section: Introductionmentioning

confidence: 99%

Neurobiomimetic constructs for intelligent unmanned systems and robotics

2014

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This paper discusses a paradigm we refer to as neurobiomimetic, which involves emulations of brain neuroanatomy and neurobiology aspects and processes. Neurobiomimetic constructs include rudimentary and down-scaled computational representations of brain regions, sub-regions, and synaptic connectivity. Many different instances of neurobiomimetic constructs are possible, depending on various aspects such as the initial conditions of synaptic connectivity, number of neuron elements in regions, connectivity specifics, and more, and we refer to these instances as 'animats'. While downscaled for computational feasibility, the animats are very large constructs; the animats implemented in this work contain over 47,000 neuron elements and over 720,000 synaptic connections. The paper outlines aspects of the animats implemented, spatial memory and learning cognitive task, the virtual-reality environment constructed to study the animat performing that task, and discussion of results. In a broad sense, we argue that the neurobiomimetic paradigm pursued in this work constitutes a particularly promising path to artificial cognition and intelligent unmanned systems. Biological brains readily cope with challenges of real-life tasks that consistently prove beyond even the most sophisticated algorithmic approaches known. At the cross-over point of neuroscience, cognitive science and computer science, paradigms such as the one pursued in this work aim to mimic the mechanisms of biological brains and as such, we argue, may lead to machines with abilities closer to those of biological species.

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“…In section 3 we describe the building traversal scenario, and in section 4 present the cognitive navigation architecture that we have developed to address the problem. To demonstrate the performance benefits of our approach, in section 5 we present an experimental comparison of a reactive only and a cognitive only approach to the building traversal problem (inspired by Braun et al [4]). Results are gathered for 30 traversals of a building with a random number (between 1 and 4) of randomly appearing (size, color and initial velocity) unstable obstacles.…”

Section: Introductionmentioning

confidence: 99%

Navigation of uncertain terrain by fusion of information from real and synthetic imagery

2012

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We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals.We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be.

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Inner rehearsal modeling for cognitive robotics

Cited by 3 publications

References 0 publications

Robot Arm Reaching Based on Inner Rehearsal

Robot Arm Reaching Based on Inner Rehearsal

Neurobiomimetic constructs for intelligent unmanned systems and robotics

Navigation of uncertain terrain by fusion of information from real and synthetic imagery

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