arXiv Computer Vision
Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors
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arXiv AI
Aug 6
GUARD: Grounding Uncertainty and Ablation-Based Risk Detection for Diffusion-Based VLAs
arXiv:2608. 04510v1 Announce Type: cross Abstract: Diffusion-based vision-language-action (VLA) policies can generate plausible actions even when their predictions are weakly grounded in the visual and language evidence defining the task.
arXiv AI
Sep 7
What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies
The paper investigates how visuomotor imitation policies fail when visually similar distractors are introduced, framing the issue as conditional visual grounding where the target changes with manipulation phase and task state. Using Action Chunking with Transformers (ACT), the authors systematically vary color and shape similarity of distractors, pinpointing failures to picking and placement stages. They then test distractor augmentation, phase‑dependent attention regularization, and appearance‑based visual prompting, which together significantly improve robustness in simulation and on a physical UR3e robot, and demonstrate similar improvements on a pretrained vision‑language‑action policy for instrument handling.
arXiv AI
Jun 3
See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs
arXiv:2606. 02735v1 Announce Type: cross Abstract: Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar tasks, the policy must often infer local execution details from coarse instructions while also deciding which parts of the image matter for control.
arXiv AI
Jun 19
ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models
arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.
arXiv Computer Vision
Oct 1
Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance
arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...
arXiv Machine Learning
Sep 1
Brain-Language-Action (BLA) Models: Language-Conditioned EEG for Robotics Control
arXiv:2608.28967v1 Announce Type: cross Abstract: Electroencephalography (EEG)-based robotic control is commonly formulated as a direct classification problem, in which electrical neural signals are...