8 citations · 16 across the 5 of their papers we have counts for
6 papers · 1 filter
Prim-LAfD: A Framework to Learn and Adapt Primitive-Based Skills from Demonstrations for Insertion Tasks
Zheng Wu, Wenzhao Lian, Changhao Wang +3
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinfo…
Learning Feasibility to Imitate Demonstrators with Different Dynamics
Zhangjie Cao, Yilun Hao, Mengxi Li +1
The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations a…
Learning Latent Actions to Control Assistive Robots
Dylan P. Losey, Hong Jun Jeon, Mengxi Li +5
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use…
Learning Human Objectives from Sequences of Physical Corrections
Mengxi Li, Alper Canberk, Dylan P. Losey +1
When personal, assistive, and interactive robots make mistakes, humans naturally and intuitively correct those mistakes through physical interaction. In simple situations, one corr…
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 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…