A predictive network architecture for a robust and smooth robot docking behavior
By Junpei Zhong, Cornelius Weber, Stefan Wermter. 2012
11 citations recorded by the source corpus.
Robots and living beings exhibit latencies in their sensorimotor processing due to mechanical and electronic or neural processing delays. A reaction typically occurs to input stimuli of the past. This is critical not only when the environment changes (e.g. moving objects) but also when the agent itself moves. An agent that does not predict while moving may need to remain static between sensory input acquisition and output response to guarantee that the response is appropriate to the percept. We propose a biologically-inspired learning model of predictive sensorimotor integration to compensate for this latency. In this model, an Elman network is developed for sensory prediction and sensory filtering; a Continuous Actor-Critic Learning Automaton (CACLA) is trained for continuous action generation. For a robot docking experiment, this architecture improves the smoothness of the robot’s sensory input and therefore results in a faster and more accurate continuous approach behavior.
Subfields: Artificial Intelligence, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Cornelius Weber
- Vincenzo Lomonaco
- Christopher Kanan
- Jun Tani
- Tyler L. Hayes
In Control and Systems Engineering
Citations in the corpus
- An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, methodology): “networks have found successful applications as predictive approaches [2], [12], [13], [20].”