Learning probabilistic models for optimal visual servo control of dynamic manipulation

We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses th...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Nikovski, D., Nourbakhsh, I.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Adaptive control Applied sciences Artificial intelligence Computer science control theory systems Control systems Control theory. Systems Cost function Exact sciences and technology Kinematics Manipulator dynamics Motion control Optimal control Pattern recognition. Digital image processing. Computational geometry Robot sensing systems Robotics Servomechanisms Servosystems
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.
DOI:	10.1109/IRDS.2002.1041533