The paper considers the transition from manual stabilization of the transport platform to an intelligent control system based on a multi-stage fuzzy logic controller, which makes it possible to reduce the problems ass...
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In this article, we propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC), designing an agent completely built from predictive...
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ISBN:
(纸本)9798350323658
In this article, we propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC), designing an agent completely built from predictive processing circuits that facilitate dynamic, online learning from sparse rewards, embodying the principles of planning-as-inference. Concretely, we craft an adaptive agent system, which we call active predictive coding (ActPC), that balances an internally-generated epistemic signal (meant to encourage intelligent exploration) with an internally-generated instrumental signal (meant to encourage goal-seeking behavior) to learn how to control various simulated robotic systems as well as a complex robotic arm using a realistic simulator, i.e., the Surreal Robotics Suite, for the block lifting task and the can pick-and-place problem. Notably, our results demonstrate that the proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backpropagation-based reinforcement learning approaches.
The world is currently experiencing an unseen increase in the number of vehicles on the road, and our traffic controlsystems have been struggling to keep up with this rapid growth. This research paper is dedicated to...
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Security system risk management problems are often solved using scenario analysis methods for risk assessment. This method models various attack scenarios on the system to be defended. Many studies consider the risks ...
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In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query ...
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In this paper, we present a comprehensive approach for designing and analyzing controlsystems with minmax constraint controllers and machine learning-based virtual sensors. By leveraging the Standard Nonlinear Operat...
The folding and transport of proteins in the Endoplasmic Reticulum (ER) of mammalian cells exhibit similarities to industrial manufacturing processes, in that they are complexsystems regulated by control mechanisms. ...
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Bearings-only tracking (BOT) problem consists in determining the position and velocities of the target from noisy angular measurements coming from a radar or optoelectronic system. It is well known that nonlinear rela...
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This document discusses the relation between H2/ H∞ control and Nash game problem for infinite Markov jump stochastic systems (MJSSs). Via a countably infinite set of coupled generalized differential Riccati equation...
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Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Add...
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ISBN:
(纸本)9781665491907
Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.
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