Early research in the field of intuitive physics provided extensive evidence that humans
succumb to common misconceptions and biases when predicting, judging, and explaining …

Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

Recent popularity of consumer-grade virtual reality devices, such as the Oculus Rift and the
HTC Vive, has enabled household users to experience highly immersive virtual …

We propose a notion of affordance that takes into account physical quantities generated
when the human body interacts with real-world objects, and introduce a learning framework …

Humans have rich understanding of liquid containers and their contents; for example, we
can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume …

The physical behavior of moving fluids is highly complex, yet people are able to interact with
them in their everyday lives with relative ease. To investigate how humans achieve this …

Open containers, ie, containers without covers, are an important and ubiquitous class of
objects in human life. In this letter, we propose a novel method for robots to “imagine” the …

In this paper, we present a probabilistic approach to explicitly infer containment relations
between objects in 3D scenes. Given an input RGB-D video, our algorithm quantizes the …

The visual perception of object affordances has emerged as a useful ingredient for building
powerful computer vision and robotic applications. In this paper we introduce a novel …