3 citations · 3 across the 6 of their papers we have counts for
7 papers
PointCast: One World Model for Rigid, Articulated, and Deformable Object Manipulation
Hantao Ye, Ross Worobel, Zhuoli Xie +4
World models are useful for robotic manipulation because robots can predict how actions change the states of objects before executing them. We present PointCast, a point-set world…
LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation
Youngjin Hong, Houjian Yu, Mingen Li +1
Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (L2A). However, this one-way paradigm o…
Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models
Mingen Li, Houjian Yu, Yixuan Huang +3
Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly ch…
Optimal Robotic Velcro Peeling with Force Feedback
Jiacheng Yuan, Changhyun Choi, Volkan Isler
We study the problem of peeling a Velcro strap from a surface using a robotic manipulator. The surface geometry is arbitrary and unknown. The robot has access to only the force fee…
Attribute-Based Robotic Grasping with Data-Efficient Adaptation
Yang Yang, Houjian Yu, Xibai Lou +2
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target…
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,…
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…