Now showing items 1-9 of 9
Sensor Adaptation and Development in Robots by Entropy Maximization of Sensory Data
A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming sensory information and to find informational relationships ...
The degree of potential damage in agonistic contests and its effects on social aggression, territoriality and display evolution
The potential for animals to inflict damage on one another whilst competing for indivisible resources is a factor of crucial importance when determining pay-offs to such animals and consequent likelihood of adopting an ...
From Unknown Sensors and Actuators to Visually Guided Movement
This paper describes a developmental system implemented on a real robot that learns a model of its own sensory and actuator apparatuses. There is no innate knowledge regarding the modality or representation of the sensoric ...
Legs that can walk: Embodiment-Based Modular Reinforcement Learning applied
Experiments to illustrate a novel methodology for reinforcement learning in embodied physical agents are described. A simulated legged robot is decomposed into structurebased modules following the authors' EMBER principles ...
All Else Being Equal Be Empowered
The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we ...
Inferring dependencies in Embodiment-based modular reinforcement learning
The state-spaces needed to describe realistic--physical embodied agents are extremely large, which presents a serious challenge to classical einforcement learning schemes. In previous work--(Jacob et al., 2005a, Jacob et ...