1. We are able to alter the state of the scene we are observing so as to learn aspects of it that are not apparent from a first look. For example, we can move our head to look from a different angle, or squeeze, push or shake an object to investigate it.
2. Humans have a very limited communication bandwidth compared to the internal computation capacity in the brain. This means that we cannot easily perform reasoning together with other humans in the way a computer cluster can share computations. It also means that communication between humans is heavily under-determined and error-prone.
3. This limited bandwidth also means that we are forced to learn from quite few examples, and are extremely good at transfer learning and abstraction of knowledge. For example, it has been shown that a child can learn to recognize an unseen animal, e.g., elephants from a single simple drawing. This indicates that humans use very different visual learning strategies than state of the art Computer Vision systems.
This has implications for how to design artificial embodied systems, especially systems that should collaborate with, learn from, and solve problems together with humans.
In the context of this, I will outline a few of the projects in my group.
In 2010, she was awarded the Koenderink Prize for fundamental contributions in Computer Vision for her ECCV 2000 article on human motion reconstruction, written together with Michael Black and David Fleet. She has written around 85 papers in the fields of Robotics, Computer Vision, Information Fusion, Machine Learning, Cognitive Science, Speech, and Human-Computer Interaction. She is mostly active within the areas of Robotics and Computer Vision, where she is an Associate Editor for IEEE TPAMI and IEEE RA-L, and regularly serves as Area Chair/Associate Editor for the major conferences. Her cross-disciplinary activities include being an Editor for PLOS ONE.