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CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors

Authors
Dachong Li, ZhuangZhuang Chen, Jin Zhang, Jianqiang Li
Source
arXiv
Published
Collection
Embodied AI
Vision Language ActionRobot ManipulationSpatial GroundingFlow MatchingLibero Benchmark
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Summary

CorridorVLA adds explicit spatial constraints to vision-language-action policies by predicting a few future end-effector movement anchors and using them to shape the action loss. On LIBERO and LIBERO-Plus, this gives consistent gains for both SmolVLA and GR00T with little architectural change.

Problem

  • Many VLA models pass spatial guidance through latent or visual features, so the action head gets location and motion information indirectly.
  • In robot manipulation, generated action trajectories can drift away from plausible spatial progress, especially under stochastic generative heads such as flow matching.
  • This matters because better spatial guidance can raise task success on long-horizon and contact-heavy manipulation benchmarks such as LIBERO and LIBERO-Plus.

Approach

  • The model predicts sparse spatial anchors: a small set of future end-effector 3D delta-positions at selected time steps in an action chunk.
  • These anchors define a corridor around the target spatial evolution. If a generated trajectory goes outside that tolerance region, the loss pushes it back; if it stays inside, a consistency term still refines it.
  • The action output is extended with end-effector delta-position fields (extra-A) so the action head and anchor predictor use the same physical quantity.
  • Training combines three parts: the base flow-matching loss, an anchor prediction loss, and a corridor regularizer with a buffer term plus an in-corridor cumulative consistency term, weighted more at lower noise levels.
  • The method is lightweight: the paper uses K=3 anchor tokens and keeps the rest of the backbone and training setup mostly unchanged.

Results

  • LIBERO, SmolVLA: success rate rises from 86.5% to 90.95% for SmolVLA-Corr, a gain of 4.45 points. Category scores move from 72.0/89.0/87.0/98.0 to 85.2/90.8/95.8/92.0 for Long/Goal/Object/Spatial.
  • LIBERO-Plus, SmolVLA: success rate rises from 45.37% to 57.74%, a gain of 12.37 points (described as 12.4% in the text). Per-category scores move from 46.53/35.89/66.2/32.85 to 49.27/55.27/72.36/54.04.
  • LIBERO-Plus, GR00T: success rate rises from 75.23% to 83.21% for GR00T-Corr, a gain of 7.98 points. Per-category scores move from 62.21/68.54/84.55/85.64 to 74.55/85.75/88.4/84.14.
  • Anchor target choice: predicting delta-positions beats absolute positions on LIBERO 4-in-1, with average success 87.5% vs 86.5% for absolute positions and 86.5% for the base model.
  • Combining components: on LIBERO, merge reaches 89.0%, +L_buf reaches 89.5%, +L_cons reaches 90.4%, and the full corridor loss reaches 90.95%, showing both corridor terms help.
  • The paper claims the added compute cost is negligible because the method mainly adds 3 future-state prediction tokens and changes the training objective.

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