Publications (15)
Learning 3D Particle-based Simulators from RGB-D Videos
William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff +4
Realistic simulation is critical for applications ranging from robotics to animation. Traditional analytic simulators sometimes struggle to capture sufficiently realistic simulatio…
Constraint-based graph network simulator
Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff +1
In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines inst…
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt +5
In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both p…
Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt +1
We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neur…
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169
General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…
Direct Motion Models for Assessing Generated Videos
Kelsey Allen, Carl Doersch, Guangyao Zhou +9
A current limitation of video generative video models is that they generate plausible looking frames, but poor motion -- an issue that is not well captured by FVD and other popular…