4 citations · 10 across the 18 of their papers we have counts for
8 papers · 1 filter
Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding
Khoa Vo, Taisei Hanyu, Yuki Ikebe +8
Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-Language Models (VLMs) for action…
Learning Human Motion with Temporally Conditional Mamba
Quang Nguyen, Tri Le, Baoru Huang +4
Learning human motion based on a time-dependent input signal presents a challenging yet impactful task with various applications. The goal of this task is to generate or estimate h…
EgoMusic-driven Human Dance Motion Estimation with Skeleton Mamba
Quang Nguyen, Nhat Le, Baoru Huang +6
Estimating human dance motion is a challenging task with various industrial applications. Recently, many efforts have focused on predicting human dance motion using either egocentr…
GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
Quang Nguyen, Tri Le, Huy Nguyen +5
Language-driven grasp detection has the potential to revolutionize human-robot interaction by allowing robots to understand and execute grasping tasks based on natural language com…
RoboDesign1M: A Large-scale Dataset for Robot Design Understanding
Tri Le, Toan Nguyen, Quang Tran +6
Robot design is a complex and time-consuming process that requires specialized expertise. Gaining a deeper understanding of robot design data can enable various applications, inclu…
Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator and Benchmarking
Minh Nhat Vu, Alexander Wachter, Gerald Ebmer +5
Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated cran…
Online Trajectory Replanner for Dynamically Grasping Irregular Objects
Minh Nhat Vu, Florian Grander, Anh Nguyen
This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a…
FedEFM: Federated Endovascular Foundation Model with Unseen Data
Tuong Do, Nghia Vu, Tudor Jianu +7
In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting cathete…