Andy Campbell
Papers
1
Total Citations
2
H-Index
1
About
Andy Campbell is a robotics researcher whose work lies at the intersection of computer vision and manipulation, with a focus on enabling robots to perform semantically aware grasping. His key research area centers on bridging the gap between simulation and real-world deployment—a critical challenge in embodied AI. In his most notable work, "Toward Sim-to-Real Directional Semantic Grasping," Campbell tackles the problem of instructing a robot to grasp a specific object from a specific direction, moving beyond simple pick-and-place. He approaches this using deep reinforcement learning, specifically a double deep Q-network (DDQN) that learns to map low-resolution RGB images from a wrist-mounted camera directly to action values. While this paper has garnered 2 citations, its significance lies in its foundational approach to directional semantic grasping, a problem that is increasingly central to applications in warehouse automation and assistive robotics. Campbell’s contributions help pave the way for robots that can understand not just *what* to pick, but *how* to pick it in a context-aware manner.
Research Focus
Key Achievements
Top Papers
- 1Toward Sim-to-Real Directional Semantic Grasping2 citations · 2020