35 citations · 73 across the 4 of their papers we have counts for
8 papers
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
Connor Schenck, Dieter Fox
In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbo…
Learning Robotic Manipulation of Granular Media
Connor Schenck, Jonathan Tompson, Dieter Fox +1
In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions.…
Reasoning About Liquids via Closed-Loop Simulation
Connor Schenck, Dieter Fox
Simulators are powerful tools for reasoning about a robot's interactions with its environment. However, when simulations diverge from reality, that reasoning becomes less useful. I…
Perceiving and Reasoning About Liquids Using Fully Convolutional Networks
Conor Schenck, Dieter Fox
Liquids are an important part of many common manipulation tasks in human environments. If we wish to have robots that can accomplish these types of tasks, they must be able to inte…
See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content
Roozbeh Mottaghi, Connor Schenck, Dieter Fox +1
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…
Visual Closed-Loop Control for Pouring Liquids
Connor Schenck, Dieter Fox
Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We…
Towards Learning to Perceive and Reason About Liquids
Connor Schenck, Dieter Fox
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and re…
Detection and Tracking of Liquids with Fully Convolutional Networks
Connor Schenck, Dieter Fox
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and re…