Reinforcement Learning for Imitating Constrained Reaching Movements
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We study a distributed approach to path planning. We focus on holonomic kinematic motion in cluttered 2D areas. The problem consists in defining the precise sequence of roto-translations of a rigid object of arbitrary shape that has to be transported from ...
We compare six different algorithms for localizing odor sources with mobile robots. Three algorithms are bio-inspired and mimic the behavior of insects when exposed to airborne pheromones. Two algorithms are based on probability and information theory, and ...
Robot PbD started about 30 years ago, growing importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training the robot to perform a task is three-fold. First and foremost, P ...
The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots that can be accomplished by everyone. When a demonstrator teaches a task to a robot, he/she shows some ways of fulfilling the task, but ...
Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic moti ...
We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the learning technique for a simple task is compared across robot groups of various ...
This study attempts to make a compact humanoid robot acquire a giant-swing motion without any robotic models by using reinforcement learning; only the interaction with environment is available. Generally, it is widely said that this type of learning method ...
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We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a ti ...
We present a miniature magnetic climbing robot with dimensions 96 x 46 x 64mm(3). With two degrees of freedom it is able to climb ferromagnetic surfaces and to make inner plane to plane transitions whatever their inclination is. This robot, named TRIPILLAR ...
Improvement over classical dynamic feedback linearization for a unicycle mobile robots is proposed. Compared to classical extension, the technique uses a higher-dimensional state extension, which allows rejecting a constant disturbance on the robot rotatio ...