title: Exploiting Vestibular Output during Learning Results in Naturally Curved Reaching Trajectories creator: Sun, Ganghua creator: Scassellati, Brian subject: Machine Learning subject: Artificial Intelligence subject: Robotics description: Teaching a humanoid robot to reach for a visual target is a complex problem in part because of the high dimensionality of the control space. In this paper, we demonstrate a biologically plausible simplification of the reaching process that replaces the degrees of freedom in the neck of the robot with sensory readings from a vestibular system. We show that this simplification introduces errors that are easily overcome by a standard learning algorithm. Furthermore, the errors that are necessarily introduced by this simplification result in reaching trajectories that are curved in the same way as human reaching trajectories. publisher: Lund University Cognitive Studies contributor: Berthouze, Luc contributor: Kaplan, Frédéric contributor: Kozima, Hideki contributor: Yano, Hiroyuki contributor: Konczak, Jürgen contributor: Metta, Giorgio contributor: Nadel, Jacqueline contributor: Sandini, Giulio contributor: Stojanov, Georgi contributor: Balkenius, Christian date: 2005 type: Conference Paper type: PeerReviewed format: application/pdf identifier: http://cogprints.org/4967/1/sun.pdf identifier: Sun, Ganghua and Scassellati, Brian (2005) Exploiting Vestibular Output during Learning Results in Naturally Curved Reaching Trajectories. [Conference Paper] relation: http://cogprints.org/4967/