Disney Research Pittsburgh

We demonstrate the efficacy of the algorithm on a theme-park type humanoid doing a drawing task, serving drink in a glass, and serving a drink placed on a tray without spilling.

The goal of this work is to enable the ubiquitous deployment of ultra-low power nodes that communicate via ambient backscatter to wired Universal Backscatter Readers, in real-world environments.

We demonstrate applications of a new actuator, the Haplug, in dynamic virtual environments.

We propose a predictor that is based on a number of category specific features ( e.g., sample size, entropy, etc.) for whether independent or joint composite detector may be more accurate for a given conjunction.

For this experiment, we designed a novel protocol to induce changes in the robot's group and study different social contexts.

We report an approach to creating on-line acoustic synchrony by using a dynamic Bayesian network learned from prior recordings of child-child play to select from a predfinened space of robot speech in response to real-time measurement of the child's prosodic features.

Motivated by the original “ghosting” work, we showcase an automatic “data-driven ghosting” method using advanced machine learning methodologies applied to a season’s worth of tracking data from a recent professional league in soccer.

We developed four different protocols to investigate human spatial behavior or trust in robots.

We present an iterative learning control algorithm for accurate task space tracking of kinematically redundant robots with stringent joint position limits and kinematic modeling errors.

In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences.