VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation

arXiv:2609.22606 · cs.RO · Submitted 2026-09-18 · Read on arXiv

cs.RO

Submitted: 2026-09-18

Updated: 2026-09-18

Terminology

• Adaptation — 18× in this paper · explained in 1 episode
• Ablation — 12× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• VLA Models — 7× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Conference on Robot Learning — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Decoder — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Mixture of Experts (MoE) — 1× in this paper · explained in 5 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• T-Rex: Tactile-Reactive Dexterous Manipulation
• FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
• CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning
• Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
• UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
• VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation
• CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation
• HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing
• OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing
• TacMamba: A Tactile History Compression Adapter Bridging Fast Reflexes and Slow VLA Reasoning
• TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation
• Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
• REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation

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