NAC: Neural Action Codec for Vision-Language-Action Models
cs.RO, cs.LG
Submitted: 2026-06-19
Updated: 2026-09-25
Project page: https://ahadjawaid.com/nac
Terminology
Sources
- High Fidelity Neural Audio Compression
- High-Fidelity Audio Compression with Improved RVQGAN
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- OAT: Ordered Action Tokenization
- Qwen3 Technical Report
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- MolmoAct: Action Reasoning Models that can Reason in Space
- Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
- UniAudio: An Audio Foundation Model Toward Universal Audio Generation
- PaliGemma: A versatile 3B VLM for transfer
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- MolmoAct2: Action Reasoning Models for Real-world Deployment
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
- ActionCodec: What Makes for Good Action Tokenizers
- FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
- Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- Theory and Experiments on Vector Quantized Autoencoders
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