Alexis Burns
Papers
1
Total Citations
4
H-Index
1
About
Alexis Burns is a pioneering researcher in multi-sensory robotic manipulation, with a focus on enabling robots to perform complex, human-like tasks through integrated perception. Her most-cited work, "Look and Listen: A Multi-Sensory Pouring Network and Dataset for Granular Media from Human Demonstrations" (2022), introduces a novel framework that combines visual and auditory feedback to allow robots to pour granular materials—such as sand or rice—into containers with precision. By mimicking the human ability to use multiple senses in a continuous feedback loop, Burns’ research addresses a critical gap in robotic dexterity: handling non-rigid, deformable media. Her work has garnered 4 citations, establishing a foundation for future studies in multi-modal learning for manipulation. Notably, Burns’ approach emphasizes learning from human demonstrations, bridging the gap between human intuition and robotic control. This contribution is significant for advancing autonomous systems in domestic and industrial settings, where tasks like cooking or material handling require adaptive, sensory-rich interaction. Burns’ research stands as a key step toward more versatile, human-inspired robotics.
Research Focus
Key Achievements
Top Papers
- 1