Ruoyu Wang

About

Ruoyu Wang is a robotics and computer vision researcher whose work spans soft robotics sensing, augmented reality for construction, and spatial reasoning in artificial intelligence. His most influential contribution, "Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing" (2020, 51 citations), tackled a long-standing challenge in soft robotics by developing a deep learning-based method to measure and model the high-dimensional 3D shapes of deformable bodies using internal vision — a significant breakthrough for flexible robotic systems. Wang has also made notable strides in human-robot collaboration within the construction industry, with his work on mobile projective augmented reality for collaborative robots (2021, 48 citations) offering practical solutions to the sector's persistent productivity challenges. His dataset contribution, SPARE3D (2020, 18 citations), advances the study of spatial reasoning in deep networks by benchmarking their ability to interpret three-view line drawings — a capability central to human-like geometric understanding. Rounding out his portfolio, Wang has explored through-wall object recognition, weakly supervised indoor robot positioning, and AR-driven construction co-robots, demonstrating a consistent focus on enabling robots to perceive and interact intelligently with complex, real-world environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing

   51 citations · 2020
2. 2
   Mobile projective augmented reality for collaborative robots in construction

   48 citations · 2021
3. 3
   SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings

   18 citations · 2020
4. 4
   Towards Mobile Projective AR for Construction Co-Robots

   6 citations · 2019
5. 5
   Through-Wall Object Recognition and Pose Estimation

   4 citations · 2019
6. 6
   Deep Weakly Supervised Positioning for Indoor Mobile Robots

   3 citations · 2021
7. 7
   Real-time Soft Body 3D Proprioception via Deep Vision-based Sensing

   3 citations · 2019

Key Collaborators

Contact & Links