The dominant practice of AI alignment assumes (1) that preferences are an adequate
representation of human values,(2) that human rationality can be understood in terms of …

Some researchers speculate that intelligent reinforcement learning (RL) agents would be
incentivized to seek resources and power in pursuit of their objectives. Other researchers …

Shared autonomy is an operational concept in which a user and an autonomous agent
collaboratively control a robotic system. It provides a number of advantages over the …

Interaction and cooperation with humans are overarching aspirations of artificial intelligence
research. Recent studies demonstrate that AI agents trained with deep reinforcement …

How can we train an assistive human-machine interface (eg, an electromyography-based
limb prosthesis) to translate a user's raw command signals into the actions of a robot or …

Assistive agents should make humans' lives easier. Classically, such assistance is studied
through the lens of inverse reinforcement learning, where an assistive agent (eg, a chatbot …

Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy
inputs (eg, webcam images of eye gaze) can be challenging, especially when it involves …

Sequential decision making, whether it is realized via reinforcement learning (RL),
supervised learning, or some form of probabilistic or otherwise symbolic planning using …

In recent years, research involving human participants has been critical to advances in
artificial intelligence (AI) and machine learning (ML), particularly in the areas of …

Assistive robot arms try to help their users perform everyday tasks. One way robots can
provide this assistance is shared autonomy. Within shared autonomy, both the human and …