Search Results for author:
Found 9 papers, 3 papers with code
Tactile-Filter: Interactive Tactile Perception for Part Mating
We evaluate our method on several part-mating tasks with novel objects using a robot equipped with a vision-based tactile sensor.
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
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.
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks
We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
HyperDynamics: Generating Expert Dynamics Models by Observation
We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification.
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples.
Fast and Guaranteed Tensor Decomposition via Sketching
Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares.
Spectral Methods for Indian Buffet Process Inference
The Indian Buffet Process is a versatile statistical tool for modeling distributions over binary matrices.
