5 papers
Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement
Michael Bloesch, Markus Wulfmeier, Philemon Brakel +8
Imitation Learning from Observation (IfO) offers a powerful way to learn behaviors at large-scale: Unlike behavior cloning or offline reinforcement learning, IfO can leverage actio…
Neural rendering enables dynamic tomography
Ivan Grega, William F. Whitney, Vikram S. Deshpande
Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static…
Linear Transformer Topological Masking with Graph Random Features
Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…
Modeling the Real World with High-Density Visual Particle Dynamics
William F. Whitney, Jacob Varley, Deepali Jain +3
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds con…
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision
Yulia Rubanova, Tatiana Lopez-Guevara, Kelsey R. Allen +3
Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously ha…