7 papers
TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
Kevin Robledo, MatÃas I. Torres Galaz, Kumar Dixhant Rai +3
Robotic systems that assist humans should be capable of adapting their behaviors to individual user preferences. For instance, users may want a robot arm to adjust the amount of fo…
Towards Balanced Behavior Cloning from Imbalanced Datasets
Sagar Parekh, Heramb Nemlekar, Dylan P. Losey
Robots should be able to learn complex behaviors from human demonstrations. In practice, these human-provided datasets are inevitably imbalanced: i.e., the human demonstrates some…
CIVIL: Causal and Intuitive Visual Imitation Learning
Yinlong Dai, Robert Ramirez Sanchez, Ryan Jeronimus +4
Today's robots attempt to learn new tasks by imitating human examples. These robots watch the human complete the task, and then try to match the actions taken by the human expert.…
PECAN: Personalizing Robot Behaviors through a Learned Canonical Space
Heramb Nemlekar, Robert Ramirez Sanchez, Dylan P. Losey
Robots should personalize how they perform tasks to match the needs of individual human users. Today's robot achieve this personalization by asking for the human's feedback in the…
L2D2: Robot Learning from 2D Drawings
Shaunak A. Mehta, Heramb Nemlekar, Hari Sumant +1
Robots should learn new tasks from humans. But how do humans convey what they want the robot to do? Existing methods largely rely on humans physically guiding the robot arm through…
RECON: Reducing Causal Confusion with Human-Placed Markers
Robert Ramirez Sanchez, Heramb Nemlekar, Shahabedin Sagheb +2
Imitation learning enables robots to learn new tasks from human examples. One fundamental limitation while learning from humans is causal confusion. Causal confusion occurs when th…