Modeling the Real World with High-Density Visual Particle Dynamics

About

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 containing 100K+ particles. To enable efficiency at this scale, we introduce a novel family of Point Cloud Transformers (PCTs) called Interlacers leveraging intertwined linear-attention Performer layers and graph-based neighbour attention layers. We demonstrate the capabilities of HD-VPD by modeling the dynamics of high degree-of-freedom bi-manual robots with two RGB-D cameras. Compared to the previous graph neural network approach, our Interlacer dynamics is twice as fast with the same prediction quality, and can achieve higher quality using 4x as many particles. We illustrate how HD-VPD can evaluate motion plan quality with robotic box pushing and can grasping tasks. See videos and particle dynamics rendered by HD-VPD at https://sites.google.com/view/hd-vpd.

William F. Whitney, Jacob Varley, Deepali Jain, Krzysztof Choromanski, Sumeet Singh, Vikas Sindhwani · 2024

Related benchmarks

Task	Dataset	Result
Rigid Body Trajectory Prediction	MOVi-A 50 frames (test)	Position RMSE (m)0.235	8
Rigid Body Trajectory Prediction	MOVi-B 50 frames (test)	Position RMSE (m)0.275	8
Rigid Body Trajectory Prediction	MOVi-A 75 frames (test)	Position RMSE (m)0.489	5
Rigid Body Trajectory Prediction	MOVi-A 100 frames (test)	Position RMSE (m)0.827	5
Rigid Body Trajectory Prediction	MOVi-B 75 frames (test)	Position RMSE (m)0.581	5
Rigid Body Trajectory Prediction	MOVi-B 100 frames (test)	Position RMSE (m)0.987	5
Rigid Body Trajectory Prediction	MOVi-Sphere 75 frames (test)	Position RMSE (m)0.51	4
Rigid Body Trajectory Prediction	MOVi-Sphere 100 frames (test)	Position RMSE (m)0.855	4
Rigid Body Trajectory Prediction	MOVi-Sphere 50 frames (test)	Position RMSE (m)0.244	4