5 citations · 5 across the 6 of their papers we have counts for
8 papers
VGM-VS: Rethinking Visual Geometry Model for High-Precision Visual Servoing
Yimin Pan, Sen Wang, You Zhou +4
We present VGM-VS, a visual servoing method built on a pretrained feed-forward visual geometry model. Given the current view and a reference image captured at the target configurat…
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
ResponsibleRobotBench: Benchmarking Responsible Robot Manipulation using Multi-modal Large Language Models
Lei Zhang, Ju Dong, Kaixin Bai +4
Recent advances in large multimodal models have enabled new opportunities in embodied AI, particularly in robotic manipulation. These models have shown strong potential in generali…
OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance
Lei Zhang, Diwen Zheng, Kaixin Bai +5
Dexterous grasp generation aims to produce grasp poses that align with task requirements and human interpretable grasp semantics. However, achieving semantically controllable dexte…
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton +2
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (…
Unknown Object Segmentation from Stereo Images
Maximilian Durner, Wout Boerdijk, Martin Sundermeyer +3
Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known…
Multi-path Learning for Object Pose Estimation Across Domains
Martin Sundermeyer, Maximilian Durner, En Yen Puang +4
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only d…
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner +2
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that i…