1 citations · 3 across the 9 of their papers we have counts for
10 papers
PREFAIL: Identifying Precursors to Failures in Robotic Lift-and-Place Tasks to Improve Task Execution Performance
Zeyu Shangguan, Rajas Chitale, Rutvik Patel +2
Non-prehensile manipulation enables flexible material handling with part carriers, but friction-based support makes high-speed motions failure-prone, while slower operation increas…
OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
Kuanning Wang, Ke Fan, Chenhao Qiu +5
Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models…
SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policy
Kuanning Wang, Yongchong Gu, Yuqian Fu +5
Scooping items with tools such as spoons and ladles is common in daily life, ranging from assistive feeding to retrieving items from environmental disaster sites. However, developi…
Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow +11
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a…
ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
Enyu Zhao, Vedant Raval, Hejia Zhang +5
Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used prima…
Sequential Multi-Object Grasping with One Dexterous Hand
Sicheng He, Zeyu Shangguan, Kuanning Wang +4
Sequentially grasping multiple objects with multi-fingered hands is common in daily life, where humans can fully leverage the dexterity of their hands to enclose multiple objects.…
Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
Zeyu Shangguan, Daniel Seita, Mohammad Rostami
Advancements in cross-modal feature extraction and integration have significantly enhanced performance in few-shot learning tasks. However, current multi-modal object detection (MM…
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
Zeyu Shangguan, Daniel Seita, Mohammad Rostami
Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features. However, existing multi-mod…
Improved Region Proposal Network for Enhanced Few-Shot Object Detection
Zeyu Shangguan, Mohammad Rostami
Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images…
Identification of Novel Classes for Improving Few-Shot Object Detection
Zeyu Shangguan, Mohammad Rostami
Conventional training of deep neural networks requires a large number of the annotated image which is a laborious and time-consuming task, particularly for rare objects. Few-shot o…