931 research outputs found
Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor
Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Model-based control requires an accurate model of the system dynamics for
precisely and safely controlling the robot in complex and dynamic environments.
Moreover, in the presence of variations in the operating conditions, the model
should be continuously refined to compensate for dynamics changes. In this
paper, we present a self-supervised learning approach that actively models the
dynamics of nonlinear robotic systems. We combine offline learning from past
experience and online learning from current robot interaction with the unknown
environment. These two ingredients enable a highly sample-efficient and
adaptive learning process, capable of accurately inferring model dynamics in
real-time even in operating regimes that greatly differ from the training
distribution. Moreover, we design an uncertainty-aware model predictive
controller that is heuristically conditioned to the aleatoric (data)
uncertainty of the learned dynamics. This controller actively chooses the
optimal control actions that (i) optimize the control performance and (ii)
improve the efficiency of online learning sample collection. We demonstrate the
effectiveness of our method through a series of challenging real-world
experiments using a quadrotor system. Our approach showcases high resilience
and generalization capabilities by consistently adapting to unseen flight
conditions, while it significantly outperforms classical and adaptive control
baselines
- …