🤖 Humanoid 🦾 Industrial & Cobot 🚚 AGV / AMR 🐕 Quadruped ⚙️ Reducers · Servos · Sensors 🚁 Drones & Autonomy 🧠 Embodied AI
Robos News
Robotics

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

arXiv:2604.21241v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidance is often injected implicitly through latent features. We propose CorridorVLA, which predicts sparse spatial anchors as incremental physical changes (e.g., end-effector $\Delta$-positions) and uses them to impose an explicit tolerance region in the training objective for action generation. The a

Published July 7, 2026 · Category: Robotics

Overview

arXiv:2604.21241v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidance is often injected implicitly through latent features. We propose CorridorVLA, which predicts sparse spatial anchors as incremental physical changes (e.g., end-effector $\Delta$-positions) and uses them to impose an explicit tolerance region in the training objective for action generation. The anchors define a tolerance corridor that guides a flow-matching action head: trajectories whose implied spatial evolution falls outside the corridor receive corrective gradients, while trajectories within the corridor are refined by a consistency objective. CorridorVLA improves SmolVLA by 4.45 percentage points on LIBERO and improves SmolVLA and GR00T by 12.37 and 7.98 percentage points, respectively, on the more challenging LIBERO-Plus benchmark. Notably, under the same single-policy 4-in-1 setting, where one policy is jointly trained and evaluated across all task suites, GR00T-Corr achieves an 83.21% success rate. These results indicate that action-aligned physical cues can provide direct and interpretable constraints for generative action policies, complementing spatial guidance encoded in visual or latent forms. Code and released model checkpoints are available at https://github.com/lidc54/corridorVLA and https://huggingface.co/lidc/CorridorVLA.

Source

Originally published at arxiv.org.

Related Articles

CD
Robos News Newsroom

Robos News covers markets, crypto and commodities for Asia & the Middle East — tier-1 desk research, AI-driven analysis, institutional-grade data. Tip our newsroom: [email protected]

Email the newsroom →
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Data may be delayed up to 15 minutes. Past performance is not indicative of future results. Consult a licensed financial advisor before making investment decisions.

Related Stories

More from News →