Autonomous learning and chaining of motor primitives using the Free Energy Principle
In this article, we apply the Free-Energy Principle to the question of motor primitives learning. An echo-state network is used to generate motor trajectories. We combine this network with a perception module and a controller that can influence its dynamics. This new compound network permits the autonomous learning of a repertoire of motor trajectories. To evaluate the repertoires built with our method, we exploit them in a handwriting task where primitives are chained to produce long-range sequences.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences
Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlations within primitives allow us to int…
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of complex motor skills and the flexible a…
A Unifying Framework for the Identification of Motor Primitives
A long-standing hypothesis in neuroscience is that the central nervous system accomplishes complex motor behaviors through the combination of a small number of motor primitives. Many studies in the last couples of decade…
Discovering Motor Programs by Recomposing Demonstrations
In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations. Current approaches to decomposing demonstrations into primitives often assume manua…
Hierarchical Reinforcement LearningReinforcement LearningLearning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors
Animals flexibly recombine a finite set of core motor primitives to meet diverse task demands, but existing behavior-segmentation methods oversimplify this process by imposing discrete syllables under restrictive generat…
Imitation LearningRepresentation Learning