93 citations · 290 across the 33 of their papers we have counts for
6 papers · 1 filter
Learning Latent Representations to Influence Multi-Agent Interaction
Annie Xie, Dylan P. Losey, Ryan Tolsma +2
Seamlessly interacting with humans or robots is hard because these agents are non-stationary. They update their policy in response to the ego agent's behavior, and the ego agent mu…
I Know What You Meant: Learning Human Objectives by (Under)estimating Their Choice Set
Ananth Jonnavittula, Dylan P. Losey
Assistive robots have the potential to help people perform everyday tasks. However, these robots first need to learn what it is their user wants them to do. Teaching assistive robo…
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…
Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences
Erdem Bıyık, Dylan P. Losey, Malayandi Palan +3
Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn re…
Shared Autonomy with Learned Latent Actions
Hong Jun Jeon, Dylan P. Losey, Dorsa Sadigh
Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grain…
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…