Yiyang Ling
Papers
1
Total Citations
8
H-Index
1
About
Yiyang Ling is a rising researcher in robotics and artificial intelligence, whose work focuses on leveraging large language models to enhance robotic learning and simulation. Ling’s major contribution lies in addressing the critical bottleneck of data scarcity for training general robotic policies. Their most cited paper, "GenSim: Generating Robotic Simulation Tasks via Large Language Models" (2023, 8 citations), introduces a novel framework that uses LLMs to automatically generate diverse and complex robotic simulation tasks, moving beyond simple scene-level variations to create rich task-level diversity. This approach significantly reduces the need for costly real-world data collection, enabling more scalable and generalizable robot training. Ling’s work represents a key step toward bridging the gap between simulation and real-world robotic applications, with potential impacts on autonomous systems and embodied AI. As an early-career researcher, Ling’s innovative use of language models for task generation marks a promising direction in robotics, earning recognition for its potential to accelerate progress in the field.
Research Focus
Key Achievements
Top Papers
- 1GenSim: Generating Robotic Simulation Tasks via Large Language Models8 citations · 2023