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Found 7 papers, 0 papers with code
Continually Learned Pavlovian Signalling Without Forgetting for Human-in-the-Loop Robotic Control
One challenge identified in these learning methods is that they can forget previously learned predictions when a user begins to successfully act upon delivered feedback.
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb.
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently.
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals.
Learned human-agent decision-making, communication and joint action in a virtual reality environment
In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task.
Communicative Capital for Prosthetic Agents
This work presents an overarching perspective on the role that machine intelligence can play in enhancing human abilities, especially those that have been diminished due to injury or illness.
Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings
Our study therefore contributes initial evidence that prediction learning and machine intelligence can benefit not just control, but also feedback from an artificial limb.
