Dachong Li | Searcharxiv

arXiv2026

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

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.

cs.RO↗

arXiv2024

$ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that $3 \times 3$ convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://github.com/lidc54/shift-wiseConv.

cs.CV↗