Yunshan Ma
Papers
1
Total Citations
4
H-Index
1
About
Yunshan Ma is a robotics researcher whose work focuses on enabling robots to interact more intelligently with unknown objects, particularly through the estimation of physical properties without relying on bulky or expensive sensors. His key research areas include robot manipulation, sensorless perception, and learning-based estimation for physical interaction. In his notable 2023 paper, "A Learning-Based Approach for Estimating Inertial Properties of Unknown Objects From Encoder Discrepancies," Ma addresses a critical challenge in small-scale robotics: how to determine an object's mass and center of mass using only encoder data, bypassing the need for commercial force/torque sensors that are often too heavy, large, or costly for compact robots. This work has already garnered attention with 4 citations, demonstrating its relevance to the field. By leveraging machine learning to extract physical insights from standard motor feedback, Ma's contribution paves the way for more affordable, lightweight, and capable robotic systems in applications ranging from assistive robotics to automated assembly. His research stands out for its practical, resourceful approach to a fundamental problem in robot dexterity.
Research Focus
Key Achievements
Top Papers
- 1