Renaud Detry

About

Renaud Detry is a robotics researcher whose work sits at the intersection of robotic manipulation, machine learning, and perception. He is best known for pioneering contributions to **grasp affordance learning** — developing probabilistic frameworks that enable robots to learn, represent, and generalize successful grasping strategies across novel objects. His most influential work introduced *grasp densities*, continuous probability density functions that map object-relative gripper configurations to their likelihood of success, providing robots with rich, transferable models of how objects can be grasped. Building on this foundation, Detry explored how robots could transfer grasping strategies across structurally similar objects by identifying reusable "prototypical parts," combining active learning with reactive control to make robotic agents more autonomous and adaptive in unstructured environments. With his most cited paper accumulating 167 citations and several works exceeding 100 citations, Detry's research has had substantial impact on the robotics community. His later work extended into tactile sensing, integrating fiber Bragg grating sensors to give robots a sense of touch — crucial for manipulation when visual information alone is insufficient. Collectively, his contributions have helped lay the groundwork for robots capable of learning from experience and generalizing intelligently to new manipulation challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   International Conference on Robotics and Automation

   167 citations · 2009
2. 2
   Combining active learning and reactive control for robot grasping

   156 citations · 2010
3. 3
   Learning a dictionary of prototypical grasp-predicting parts from grasping experience

   100 citations · 2013
4. 4
   Learning Grasp Affordance Densities

   80 citations · 2011
5. 5
   Learning object-specific grasp affordance densities

   70 citations · 2009
6. 6
   Generalizing grasps across partly similar objects

   59 citations · 2012
7. 7
   Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor

   46 citations · 2019
8. 8
   Learning tactile characterizations of object- and pose-specific grasps

   42 citations · 2011
9. 9
   Refining grasp affordance models by experience

   40 citations · 2010
10. 10
    Development of Object and Grasping Knowledge by Robot Exploration

    36 citations · 2010

Key Collaborators

Contact & Links