Daniel Renninghoff
Papers
1
Total Citations
23
H-Index
1
About
Daniel Renninghoff is a robotics researcher whose work lies at the intersection of perception, manipulation, and machine learning, with a particular focus on enabling robots to grasp unknown objects in unstructured environments. His most cited work, "Visuo-Haptic Grasping of Unknown Objects based on Gaussian Process Implicit Surfaces and Deep Learning" (2019, 23 citations), introduces a pioneering framework that fuses visual and tactile data using Gaussian Process Implicit Surfaces. This approach allows a humanoid robot to plan and execute grasps on objects it has never seen before, overcoming the challenges of noisy sensor data by integrating sensing, planning, and action into a single pipeline. By combining deep learning with probabilistic surface modeling, Renninghoff's research directly addresses a critical bottleneck in autonomous manipulation: the ability to handle novel objects without prior models. His contributions are particularly impactful for the development of more dexterous and adaptive humanoid robots, bridging the gap between perception and physical interaction. Renninghoff's work is a key reference for researchers in robotic grasping, haptics, and sensor fusion, demonstrating how probabilistic methods can enhance real-world robotic performance.
Research Focus
Key Achievements
Top Papers
- 1