Attunement and Affordance Learning in Infants

Learning grasp affordance densities

We address the issue of learning and representing object grasp affordance models. We model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability. The underlying function representation is nonparametric and relies on kernel density estimation to provide a continuous model. Grasp densities are learned ...

Robotic framework for affordance learning

The report summarises the research progress since the last thesis group meeting held in April this year. As I have mentioned in my previous reports[5, 6] I intend to build a robotic framework, which will allow a robot to incrementally learn object affordances through active observation and interaction with the “toy-world”[5, 7]. The toy-world is an appropriately constrained environment [5], suc...

Object-object interaction affordance learning

This paper presents a novel object–object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human–object–objectway. The innate interaction-affordance knowledge of the paired objects...

Representing and learning affordance-based behaviors

Active Affordance Learning in Continuous State and Action Spaces

Learning object affordances and manipulation skills is essential for developing cognitive service robots. We propose an active affordance learning approach in continuous state and action spaces without manual discretization of states or exploratory motor primitives. During exploration in the action space, the robot learns a forward model to predict action effects. It simultaneously updates the ...