A Benchmarking Study of Vision-Based Robotic Grasping Algorithms: A Comparative Analysis
We present a benchmarking study of vision-based robotic grasping algorithms and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithms strengths and weaknesses under different experimental conditions.
Evidence notes
- The study compares two machine-learning and two analytical vision-based robotic grasping algorithms under a common benchmarking protocol.
- Physical experiments use the Stereolabs ZED 2i as the perception platform.
- The comparison identifies strengths and weaknesses across different manipulation conditions.
Company context
Stereolabs develops ZED stereo cameras, depth perception hardware and spatial AI software for robots, drones and autonomous systems.