8 citations · 16 across the 6 of their papers we have counts for
7 papers
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 Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation
Mengxi Li, Rika Antonova, Dorsa Sadigh +1
When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as kni…
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…