3 citations · 5 across the 6 of their papers we have counts for
6 papers
Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues
Mingen Li, Changhyun Choi
Manipulation of deformable Linear objects (DLOs), including iron wire, rubber, silk, and nylon rope, is ubiquitous in daily life. These objects exhibit diverse physical properties,…
SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics
Alireza Rezazadeh, Athreyi Badithela, Karthik Desingh +1
Learning multi-object dynamics from visual data using unsupervised techniques is challenging due to the need for robust, object representations that can be learned through robot in…
Adversarial Object Rearrangement in Constrained Environments with Heterogeneous Graph Neural Networks
Xibai Lou, Houjian Yu, Ross Worobel +2
Adversarial object rearrangement in the real world (e.g., previously unseen or oversized items in kitchens and stores) could benefit from understanding task scenes, which inherentl…
IOSG: Image-driven Object Searching and Grasping
Houjian Yu, Xibai Lou, Yang Yang +1
When robots retrieve specific objects from cluttered scenes, such as home and warehouse environments, the target objects are often partially occluded or completely hidden. Robots a…
Active Mass Distribution Estimation from Tactile Feedback
Jiacheng Yuan, Changhyun Choi, Ellad B. Tadmor +1
In this work, we present a method to estimate the mass distribution of a rigid object through robotic interactions and tactile feedback. This is a challenging problem because of th…
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach
Houjian Yu, Changhyun Choi
Instance segmentation with unseen objects is a challenging problem in unstructured environments. To solve this problem, we propose a robot learning approach to actively interact wi…