82 citations · 85 across the 17 of their papers we have counts for
7 papers · 1 filter
Embodied Snap: Octopus-Inspired Distributed Reach-and-Attach with a Speed-Limited Soft Arm
Linxin Hou, Zhihang Qin, Heyang Zou +5
Reach-and-attach of soft robotic arms with passive suction requires accurate targeting and sufficient contact speed, yet geared actuators can impose a speed limit that improved tra…
Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
Denys Iliash, Jiayi Liu, Egor Fokin +4
We present Artiverse, a diverse and physically grounded dataset of high-quality articulated 3D objects designed for realistic functional modeling and simulation. Artiverse contains…
JRM: Joint Reconstruction Model for Multiple Objects without Alignment
Qirui Wu, Yawar Siddiqui, Duncan Frost +6
Object-centric reconstruction seeks to recover the 3D structure of a scene through composition of independent objects. While this independence can simplify modeling, it discards st…
Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
Qirui Wu, Shizhou Zhang, De Cheng +4
Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, prim…
Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction
Linxin Hou, Qirui Wu, Zhihang Qin +2
This paper proposes SoftGM, an octopus-inspired distributed control architecture for segmented soft robotic arms that learn to reach targets in contact-rich environments using onli…
ShapeR: Robust Conditional 3D Shape Generation from Casual Captures
Yawar Siddiqui, Duncan Frost, Samir Aroudj +9
Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely…
YOLO-IOD: Towards Real Time Incremental Object Detection
Shizhou Zhang, Xueqiang Lv, Yinghui Xing +4
Current methods for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO dete…