5 papers
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
Hye-Jung Yoon, Juno Kim, Yesol Park +2
Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these…
PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
Anmol Gupta, Weiwei Gu, Omkar Patil +2
Articulation modeling enables robots to learn joint parameters of articulated objects for effective manipulation which can then be used downstream for skill learning or planning. E…
ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
Byung-ju Kim, Jinu Pahk, Chungwoo Lee +6
Behavior-cloning based visuomotor policies enable precise manipulation but often inherit the slow, cautious tempo of human demonstrations, limiting practical deployment. However, p…
Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects
Anmol Gupta, Weiwei Gu, Omkar Patil +2
As robots become more generalized and deployed in diverse environments, they must interact with complex objects, many with multiple independent joints or degrees of freedom (DoF) r…
Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter
Seunghyeon Lim, Youngjae Yoo, Jun Ki Lee +1
In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiment…