57 citations · 120 across the 5 of their papers we have counts for
5 papers
Learning User-Preferred Mappings for Intuitive Robot Control
Mengxi Li, Dylan P. Losey, Jeannette Bohg +1
When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically…
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
Minae Kwon, Erdem Biyik, Aditi Talati +3
In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and…
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…
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;…
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…