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Kuan-Ting Yu

8 accepted papers

2026

GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks

ICRA 2026poster

Robotic food scooping is a critical manipulation skill for food preparation and service robots. However, existing robot learning algorithms, especially learn-from-demonstration methods, still struggle to handle diverse and dynamic food states, which often results in spillage and reduced reliability.…

2024

MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images

CVPR 2024poster

Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category hampering their scalability in real applications when confronted with previously unseen objects. In this paper we propose MatchU a Fuse-Describe-Match strategy for 6…

Cited by 10SourcePDFScholar
2021

Tactile SLAM: Real-time inference of shape and pose from planar pushing

ICRA 2021poster

Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and po…

Cited by 62SourceScholar
2018

Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object

IROS 2018poster

We develop a real-time state estimation system to recover the pose and contact formation of an object relative to its environment. In this paper, we focus on the application of inserting an object picked by a suction cup into a tight space, a key technology for robotic packaging. We propose a framew…

Cited by 39SourceScholar
2018

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

ICRA 2018poster

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel obj…

Cited by 848SourcecodeScholar
2017

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

ICRA 2017poster

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occl…

Cited by 593SourcecodeScholar
2016

More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

IROS 2016poster

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects m…

Cited by 218SourceScholar