Benjamin A. Newman

I am a PhD student in the Robotics Institute at Carnegie Mellon Univeristy, where I work on assistive human robot-interaction.

At CMU I am a member of the Human And Robot Partners (HARP) Lab where I am advised by Henny Admoni and Kris Kitani. Broadly, I am interested in developing seamlessly assistive home robots. Specifically, I want to create assistive robots that are able to understand and reason about the consequences of their actions on the people they assist. I am fortunate to be funded in part by the NSF GRFP.

Recently I completed an internship at Meta's Reality Labs. Here, I studied the effect of visual and optimal assistance on people as they completed a complex house cleaning task in an XR simulation built in Habitat. Here, I worked with Ruta Desai and Kevin Carlberg.

I completed my undergrad at Indiana University, Bloomington in 2016 where I obtained a BS in Computer Science and a BS in Cognitive Science. While there I was fortunate to work with David Crandall, Chen Yu, and Kris Hauser.

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

I'm interested in human-robot interaction, assistive technologies, machine learning and reinforcement learning. My research focuses on how we can develop human-robot systems that lead to successful assistive interactions.

We define assistance as a perspective on human-robot interaction and provide cross-domain design axes that are critical to consider when developing assistive robotics. We support these through a broad review of recent assistive robotics research.

We present a multi-modal dataset of eye gaze, joystick activation, egocentric video, robot motion, and arm electromyography taken during a human-robot co-manipulation task under varying degrees of robotic assistance.

* denotes equal contribution

We explore how robot motion that is expressed in advance of an expected phenomenon (e.g. a robot reaching for an object it expects the user will want) affects the eventual decision the person makes.

* denotes equal contribution

We examine how presenting users with optimal routing assistance through a visual display would affect their ability and sense of agency when completing a complex object rearrangement task.

We develop a portable haptic device that aids visually impaired users navigte in real world environments.

* denotes equal contribution

We study how cross-situational statistics drive visual attention. Specifically, we examine how attention differs towards objects that are displayed infrequently versus those that are displayed frequently.

We attempt to discover joint hand and eye gaze primitives for human robot co-manipulataion in an assisted eating task that could be useful for user goal recognition.

Thank you Jon Barron for creating and open-sourcing a fantastic website!