56 citations · 265 across the 35 of their papers we have counts for
5 papers · 1 filter
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Michael R. Dawson, Adam S. R. Parker, Heather E. Williams +4
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple…
A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning
Kory W. Mathewson, Patrick M. Pilarski
Interactive machine learning (IML) is a field of research that explores how to leverage both human and computational abilities in decision making systems. IML represents a collabor…
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
Kory W. Mathewson, Patrick M. Pilarski
This paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each o…
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning
Kory W. Mathewson, Patrick M. Pilarski
This paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement…
Face valuing: Training user interfaces with facial expressions and reinforcement learning
Vivek Veeriah, Patrick M. Pilarski, Richard S. Sutton
An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn a…