Affordance Realization in Climbing: Learning and Transfer

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

Environmental Design Shapes Perceptual-motor Exploration, Learning, and Transfer in Climbing

This study investigated how environmental design shapes perceptual-motor exploration, when meta-stable regions of performance are created. Here, we examined how creating meta-stable regions of performance could destabilize pre-existing skills, favoring greater exploration of performance environments, exemplified in this study by climbing surfaces. In this investigation we manipulated hold orien...