5.7k citations · 15.5k across the 75 of their papers we have counts for
19 papers · 1 filter
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac +2
Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we ha…
Vitruvion: A Generative Model of Parametric CAD Sketches
Ari Seff, Wenda Zhou, Nick Richardson +1
Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key…
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Dibya Ghosh, Jad Rahme, Aviral Kumar +3
Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL pr…
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz +1
In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraint…
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
Ari Seff, Yaniv Ovadia, Wenda Zhou +1
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as…
Randomized Automatic Differentiation
Deniz Oktay, Nick McGreivy, Joshua Aduol +2
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to comput…