NORM-Nav is a zero-shot framework that parses natural language behavioral constraints with an LLM, grounds them via vision-LiDAR, and encodes them as multi-layer costmaps for grid-based robot navigation.
Cast: Counterfactual labels improve instruction following in vision-language- action models
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Generalist robots should be able to understand and follow user instructions. Despite providing a powerful architecture for mapping open-vocabulary language instructions to robot actions, current vision-language-action (VLA) models struggle to follow fine-grained commands. One cause for this is a lack of semantic diversity and language grounding in existing robot datasets and, specifically, a lack of fine-grained task diversity for similar observations. To address this, we present a novel method to augment existing robot datasets by leveraging vision-language models to create counterfactual labels. By augmenting existing datasets with these labels, we increase the diversity and granularity of language grounding for robot datasets, ultimately improving the language-following capabilities of VLAs. We evaluate the resulting model's ability to follow language instructions, ranging from simple object-centric commands to complex referential tasks, by conducting vision-language navigation experiments in 3 different indoor and outdoor environments. Our experiments show that counterfactual relabeling (without additional data collection) significantly improves instruction-following in VLA policies, outperforming state-of-the-art methods and doubling the success rate compared to VLAs trained on unaugmented data. We also evaluate our method for manipulation VLAs and find a similar gain in performance on tasks with distractors.
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cs.RO 4years
2026 4verdicts
UNVERDICTED 4roles
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Sentinel-VLA adds metacognitive status monitoring to VLA models for on-demand reasoning and error recovery, reporting over 30% higher real-world task success than prior SOTA.
Steerable VLAs trained on rich synthetic commands at subtask, motion, and pixel levels enable VLMs to steer robot behavior more effectively, outperforming prior hierarchical baselines on real-world manipulation and generalization tasks.
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
citing papers explorer
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NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints
NORM-Nav is a zero-shot framework that parses natural language behavioral constraints with an LLM, grounds them via vision-LiDAR, and encodes them as multi-layer costmaps for grid-based robot navigation.
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Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
Sentinel-VLA adds metacognitive status monitoring to VLA models for on-demand reasoning and error recovery, reporting over 30% higher real-world task success than prior SOTA.
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Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control
Steerable VLAs trained on rich synthetic commands at subtask, motion, and pixel levels enable VLMs to steer robot behavior more effectively, outperforming prior hierarchical baselines on real-world manipulation and generalization tasks.
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Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.