128 citations · 164 across the 8 of their papers we have counts for
8 papers
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen +6
General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g.,…
Are Deep Neural Networks SMARTer than Second Graders?
Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit +2
Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generati…
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith +5
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects…
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
Daniel M. Bear, Elias Wang, Damian Mrowca +12
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion,…
AGENT: A Benchmark for Core Psychological Reasoning
Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan +6
For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability t…
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Unsupervised Discovery of 3D Physical Objects from Video
Yilun Du, Kevin Smith, Tomer Ulman +2
We study the problem of unsupervised physical object discovery. While existing frameworks aim to decompose scenes into 2D segments based off each object's appearance, we explore ho…
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Kelsey R. Allen, Kevin A. Smith, Joshua B. Tenenbaum
Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their cap…