Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
cs.RO, cs.LG
Submitted: 2026-06-10
Updated: 2026-10-16
Comments: CoRL 2026
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- VLAs are Confined yet Capable of Generalizing to Novel Instructions
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
- MolmoAct: Action Reasoning Models that can Reason in Space
- MolmoAct2: Action Reasoning Models for Real-world Deployment
- Do What You Say: Steering Vision-Language-Action Models via Runtime Reasoning-Action Alignment Verification
- A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search
- Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
- Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control
- Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
- Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training
- ReSteer: Quantifying and Refining the Steerability of Multitask Robot Policies
- SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
- Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations
- Mechanistic interpretability for steering vision-language-action models
- Observing and Controlling Features in Vision-Language-Action Models
- SAFE: Multitask Failure Detection for Vision-Language-Action Models
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
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