LMSS @ Cornell Tech: Shuran Song (Columbia University)
Active scene understanding with robot interactions
Most computer vision algorithms are built with the goal to understand the physical world. Yet, as reflected in standard vision benchmarks and datasets, these algorithms continue to assume the role of a passive observer — only watching static images or videos, without the ability to interact with the environment. This assumption becomes a fundamental limitation for applications in robotics, where systems are intrinsically built to actively engage with the physical world.
In this talk, I will present some recent work from my group that demonstrates how we can enable robots to leverage their ability to interact with the environment in order to better understand what they see: from discovering objects’ identity and 3D geometry to discovering physical properties of novel objects through different dynamic interactions. We will demonstrate how the learned knowledge can be used to facilitate downstream manipulation tasks. Finally, I will discuss a few open research directions in the area of active scene understanding.
Shuran Song is an assistant professor in the Department of Computer Science at Columbia University. Before that, she received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST in 2013. Her research interests lie at the intersection of computer vision and robotics. She received the RSS best system paper in 2019, the Best Manipulation System Paper Award from Amazon in 2018, and has been finalist for best paper awards at conferences ICRA’20, CVPR’19 and IROS’18.