1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Detalles del recurso

Descripción

Swarm robotics investigates how a large population of robots with simple actuation and limited sensors can collectively solve complex tasks. One particular interesting application with robot swarms is autonomous object assembly. Such tasks have been solved successfully with robot swarms that are controlled by a human operator using a light source. In this paper, we present a method to solve such assembly tasks autonomously based on policy search methods. We split the assembly process in two subtasks: generating a high-level assembly plan and learning a low-level object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution. Learning the object movement policy is challenging as it depends on the complex state of the swarm which consists of an individual state for each agent. To approach this problem, we introduce a representation of the swarm which is based on Hilbert space embeddings of distributions. This representation is invariant to the number of agents in the swarm as well as to the allocation of an agent to its position in the swarm. These invariances make the learned policy robust to changes in the swarm and also reduce the search space for the policy search method significantly. We show that the resulting system is able to solve assembly tasks with varying object shapes in multiple simulation scenarios and evaluate the robustness of our representation to changes in the swarm size. Furthermore, we demonstrate that the policies learned in simulation are robust enough to be transferred to real robots.

Pertenece a

Faculty of Technology ePrints Service  

Autor(es)

Gebhardt, G. H. W. -  Daun, K. -  Schnaubelt, M. -  Neumann, G. - 

Id.: 71215140

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveG760 Machine Learning - 

Tipo de recurso: Conference or Workshop contribution  -  NonPeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://eprints.lincoln.ac.uk/31674/

Fecha de contribución: 14-abr-2018

Contacto:

Localización:
* Gebhardt, G. H. W. and Daun, K. and Schnaubelt, M. and Neumann, G. (2018) Learning robust policies for object manipulation with robot swarms. In: IEEE International Conference on Robotics and Automation, 21 - 25 May 2018, Brisbane, Australia.

Otros recursos del mismo autor(es)

  1. Learning coupled forward-inverse models with combined prediction errors Challenging tasks in unstructured environments require robots to learn complex models. Given a large...
  2. Sample and feedback efficient hierarchical reinforcement learning from human preferences While reinforcement learning has led to promising results in robotics, defining an informative rewar...
  3. Robust learning of object assembly tasks with an invariant representation of robot swarms — Swarm robotics investigates how a large population of robots with simple actuation and limited sen...
  4. Hybrid control trajectory optimization under uncertainty Trajectory optimization is a fundamental problem in robotics. While optimization of continuous contr...
  5. Contextual CMA-ES Many stochastic search algorithms are designed to optimize a fixed objective function to learn a tas...

Otros recursos de la mismacolección

  1. Geometric techniques for trajectory planning and chaos control of a bio-inspired autogenetic capsule robot Biological systems achieve energy efficient and adaptive behaviours through extensive internal and e...
  2. Making better use of local data in flood frequency estimation Flood frequency estimates are an essential part of flood risk management. They are an important ingr...
  3. Digital catchment observatories: a platform for engagement and knowledge exchange between catchment scientists, policy makers, and local communities Increasing pressures on the hydrological cycle from our changing planet have led to calls for a refo...
  4. Rapid post-settlement floodplain accumulation in Northland, New Zealand Many river systems, within New Zealand and globally, have experienced rapid acceleration in floodpla...
  5. Anthropogenic alluvium: an evidence-based meta-analysis for the UK Holocene An exploratory meta-analysis of 14C-dated Holocene anthropogenic alluvium (AA) in the UK is presente...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.