activity
20172026
most citedTrajectory Deformations from Physical Human-Robot Interaction

93 citations · 290 across the 33 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.RO2019★ 8 cited

Learning from My Partner's Actions: Roles in Decentralized Robot Teams

Dylan P. Losey, Mengxi Li, Jeannette Bohg +1

When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpr…

cs.RO2019★ 54 cited

Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi +2

Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response;…

cs.RO2019

Robots that Take Advantage of Human Trust

Dylan P. Losey, Dorsa Sadigh

Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot's objective is, we int…

cs.RO2019

Controlling Assistive Robots with Learned Latent Actions

Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar +2

Assistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms use…

cs.RO2019

Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction

Dylan P. Losey, Marcia K. O'Malley

During human-robot interaction (HRI), we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we…