Spatial Forcing: Implicit Spatial Representation Alignment for
  Vision-language-action Model
Vision-language-action (VLA) models have recently shown strong potential in
enabling robots to follow language instructions and execute precise actions.
However, most VLAs are built upon vision-language models pretrained solely on
2D data, which lack accurate spatial awareness and hinder their ability to
operate in the 3D physical world. Existing solutions attempt to incorporate
explicit 3D sensor inputs such as depth maps or point clouds, but these
approaches face challenges due to sensor noise, hardware heterogeneity, and
incomplete depth coverage in existing datasets. Alternative methods that
estimate 3D cues from 2D images also suffer from the limited performance of
depth estimators.We propose Spatial Forcing (SF), a simple yet effective
alignment strategy that implicitly forces VLA models to develop spatial
comprehension capabilities without relying on explicit 3D inputs or depth
estimators. SF aligns intermediate visual embeddings of VLAs with geometric
representations produced by pretrained 3D foundation models. By enforcing
alignment at intermediate layers, SF guides VLAs to encode richer spatial
representations that enhance action precision.Extensive experiments in
simulation and real-world environments demonstrate that SF achieves
state-of-the-art results, surpassing both 2D- and 3D-based VLAs. SF further
accelerates training by up to 3.8x and improves data efficiency across diverse
robotic tasks. Project page is at https://spatial-forcing.github.io/
68ef0057486b78128f0e33b0
Fuhao Li
68ef0057486b78128f0e33b1
Wenxuan Song
68ef0057486b78128f0e33b2
Han Zhao
claimed_verified
2025-10-18T04:50:15.257Z
646dbbc8075bbcc48ddcecbf
https://cdn-avatars.huggingface.co/v1/production/uploads/646dbbc8075bbcc48ddcecbf/V52Em-78O5F3QxRbRwG5O.jpeg
Han Zhao
user
han1997
68ef0057486b78128f0e33b3
Jingbo Wang
claimed_verified
2025-10-15T03:12:53.766Z
66a4ed2a9ba24c30408441b0
https://cdn-avatars.huggingface.co/v1/production/uploads/66a4ed2a9ba24c30408441b0/K9Z4MK3Do2CXjXr7XQDdu.png
Jingbo Wang
user
hhhJB
68ef0057486b78128f0e33b4
Pengxiang Ding
68ef0057486b78128f0e33b5
Donglin Wang
68ef0057486b78128f0e33b6
Long Zeng
68ef0057486b78128f0e33b7
Haoang Li
Spatial Forcing: Implicit Spatial Representation Alignment for
  Vision-language-action Model
Vision-language-action (VLA) models have recently shown strong potential in
enabling robots to follow language instructions and execute precise actions.
However, most VLAs are built upon vision-language models pretrained solely on
2D data, which lack accurate spatial awareness and hinder their ability to
operate in the 3D physical world. Existing solutions attempt to incorporate
explicit 3D sensor inputs such as depth maps or point clouds, but these
approaches face challenges due to sensor noise, hardware heterogeneity, and
incomplete depth coverage in existing datasets. Alternative methods that
estimate 3D cues from 2D images also suffer from the limited performance of
depth estimators.We propose Spatial Forcing (SF), a simple yet effective
alignment strategy that implicitly forces VLA models to develop spatial
comprehension capabilities without relying on explicit 3D inputs or depth
estimators. SF aligns intermediate visual embeddings of VLAs with geometric
representations produced by pretrained 3D foundation models. By enforcing
alignment at intermediate layers, SF guides VLAs to encode richer spatial
representations that enhance action precision.Extensive experiments in
simulation and real-world environments demonstrate that SF achieves
state-of-the-art results, surpassing both 2D- and 3D-based VLAs. SF further
accelerates training by up to 3.8x and improves data efficiency across diverse
robotic tasks. Project page is at https://spatial-forcing.github.io/