Ruinian Xu

About

Ruinian Xu is a robotics and computer vision researcher whose work centers on enabling intelligent robotic manipulation through deep learning, affordance understanding, and grasp detection. His most influential contribution, "Real-World Multiobject, Multigrasp Detection" (2018, 447 citations), introduced a novel deep learning architecture that reframes grasp prediction as a classification problem using null hypothesis competition rather than regression, significantly advancing robotic grasping in realistic, cluttered environments. Building on this foundation, Xu developed frameworks for affordance segmentation and keypoint detection, notably demonstrating that synthetic training images can substitute for costly real-world annotations — a practically impactful insight reflected across multiple papers. His affordance-focused research culminates in systems capable of detecting, ranking, and acting upon object functionalities for manipulation tasks involving novel objects. More recently, his work on "FoundationGraspFoundationGrasp" (2025) leverages large foundation models to generalize task-oriented grasping, bridging semantic reasoning with geometric understanding. Xu has also contributed meaningfully to assistive robotics, designing augmented reality and tongue-drive interfaces to empower individuals with physical disabilities. With over 700 cumulative citations, his research has meaningfully shaped how robots perceive, reason about, and interact with objects in unstructured real-world settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-World Multiobject, Multigrasp Detection

   447 citations · 2018
2. 2
   Learning Affordance Segmentation for Real-World Robotic Manipulation via Synthetic Images

   63 citations · 2019
3. 3
   An Affordance Keypoint Detection Network for Robot Manipulation

   52 citations · 2021
4. 4
   Deep Grasp: Detection and Localization of Grasps with Deep Neural Networks.

   34 citations · 2018
5. 5
   Toward Affordance Detection and Ranking on Novel Objects for Real-World Robotic Manipulation

   32 citations · 2019
6. 6
   The Helping Hand: An Assistive Manipulation Framework Using Augmented Reality and Tongue-Drive Interfaces

   23 citations · 2018
7. 7
   FoundationGrasp: Generalizable Task-Oriented Grasping With Foundation Models

   22 citations · 2025
8. 8
   Real-world Multi-object, Multi-grasp Detection

   14 citations · 2018
9. 9
   Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

   13 citations · 2023
10. 10
    Hands-Free Assistive Manipulator Using Augmented Reality and Tongue Drive System

    5 citations · 2018

Key Collaborators

Contact & Links