Mapping point clouds to Fourier features improves high-precision imitation learning policies on RoboCasa, ManiSkill3, and real-robot tasks compared with Cartesian inputs.
Pointvla: Injecting the 3d world into vision-language-action models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Sparse2Act pretrains sparse 3D encoders via masked action-alignment supervision, yielding reusable representations that reach 86.9% success on LIBERO-10 and enable cross-domain transfer.
citing papers explorer
-
Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
Mapping point clouds to Fourier features improves high-precision imitation learning policies on RoboCasa, ManiSkill3, and real-robot tasks compared with Cartesian inputs.
-
Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Sparse2Act pretrains sparse 3D encoders via masked action-alignment supervision, yielding reusable representations that reach 86.9% success on LIBERO-10 and enable cross-domain transfer.