C
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.
Papers
More filters
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
Vivian Chu,Ian McMahon,Lorenzo Riano,Craig G. McDonald,Qin He,Jorge Martinez Perez-Tejada,Michael Arrigo,Trevor Darrell,Katherine J. Kuchenbecker +8 more
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
Vivian Chu,Ian McMahon,Lorenzo Riano,Craig G. McDonald,Qin He,Jorge Martinez Perez-Tejada,Michael Arrigo,Naomi T. Fitter,John C. Nappo,Trevor Darrell,Katherine J. Kuchenbecker +10 more
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.