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Craig G. McDonald

Researcher at Rice University

Publications -  16
Citations -  594

Craig G. McDonald is an academic researcher from Rice University. The author has contributed to research in topics: Robot & Haptic technology. The author has an hindex of 7, co-authored 16 publications receiving 415 citations. Previous affiliations of Craig G. McDonald include University of Pennsylvania.

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Journal ArticleDOI

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction

TL;DR: This review provides a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling ofhuman and robot is a vital aspect.
Journal ArticleDOI

Robotic learning of haptic adjectives through physical interaction

TL;DR: A Willow Garage PR2 robot is augmented with a pair of SynTouch BioTac sensors to capture rich tactile signals during the execution of four exploratory procedures on 60 household objects and several machine-learning algorithms were developed to discover the meaning of each adjective from the robot's sensory data.
Proceedings ArticleDOI

Using robotic exploratory procedures to learn the meaning of haptic adjectives

TL;DR: By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, this research created a platform uniquely capable of feeling the physical properties of everyday objects.
Journal ArticleDOI

A Time-Domain Approach to Control of Series Elastic Actuators: Adaptive Torque and Passivity-Based Impedance Control

TL;DR: In this paper, a model reference adaptive controller is developed, which requires no prior knowledge of system parameters and can specify desired closed-loop torque characteristics, and conditions for passivity when augmenting any stable SEA torque controller with an arbitrary impedance.
Journal ArticleDOI

A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury

TL;DR: The ability of an EMG classifier to discern intended direction of motion in single-degree-of-freedom (DoF) and multi-DoF control modes was assessed for usability in a therapy-like setting, and results are encouraging for the future use of myoelectric interfaces in robotic rehabilitation for SCI.