Learning the rules of a game: Neural conditioning in human-robot interaction with delayed rewards
By Andrea Soltoggio, René Felix Reinhart, Andre Lemme, Jochen J. Steil. 2013
11 citations recorded by the source corpus.
Learning in human-robot interaction, as well as in human-to-human situations, is characterised by noisy stimuli, variable timing of stimuli and actions, and delayed rewards. A recent model of neural learning, based on modulated plasticity, suggested the use of rare correlations and eligibility traces to model conditioning in real-world situations with uncertain timing. The current study tests neural learning with rare correlations in a human-robot realistic teaching scenario. The humanoid robot iCub learns the rules of the game rock-paper-scissors while playing with a human tutor. The feedback of the tutor is often delayed, missing, or at times even incorrect. Nevertheless, the neural system learns with great robustness and similar performance both in simulation and in robotic experiments. The results demonstrate the efficacy of the plasticity rule based on rare correlations in implementing robotic neural conditioning.
Subfields: Artificial Intelligence, Cognitive Neuroscience
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
- Cornelius Weber
- Vincenzo Lomonaco
- Luiza Mici
- Christopher Kanan
- Tyler L. Hayes
- Jun Tani
In Cognitive Neuroscience
Citations in the corpus
- Self-organizing neural integration of pose-motion features for human action recognition (2015, background): “For instance, the visual detection of dangerous events for assistive robotics such as fall events (Parisi and Wermter, 2013, 2015), and the recognition of actions with learning robots in HRI scenarios (Soltoggio et al., 2013a,b; Barros et al., 2014; Parisi et al., 2014a,b).”